Methods for inhibition of cell proliferation, synergistic transcription modules and uses thereof

ABSTRACT

The invention provides for methods for treating nervous system cancers in a subject. The invention further provides methods for treating nervous system tumor cell invasion, migration, proliferation, and angiogenesis associated with nervous system tumors.

This application is a continuation-in-part of International ApplicationPCT/US2010/047556, filed on Sep. 1, 2010, which claims priority to U.S.Provisional Application Nos. 61/238,964, filed on Sep. 1, 2009;61/244,816, filed on Sep. 22, 2009; and 61/294,190, filed Jan. 12, 2010,the contents of each of which are incorporated herein by reference intheir entireties.

GOVERNMENT SUPPORT

The work described herein was supported in whole, or in part, byNational Cancer Institute Grant Nos. R01-CA85628 and R01-CA101644,National Institute of Allergy and Infectious Diseases grant No.R01-A1066116, and National Centers for Biomedical Computing NIH RoadmapInitiative grant No. U54CA121852. Thus, the United States Government hascertain rights to the invention.

All patents, patent applications and publications cited herein arehereby incorporated by reference in their entirety. The disclosures ofthese publications in their entireties are hereby incorporated byreference into this application in order to more fully describe thestate of the art as known to those skilled therein as of the date of theinvention described and claimed herein.

This patent disclosure contains material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent document or the patent disclosureas it appears in the U.S. Patent and Trademark Office patent file orrecords, but otherwise reserves any and all copyright rights.

BACKGROUND OF THE INVENTION

Glioma is a lethal disease with multiple genetic and epigeneticalterations. These changes work in concert in a coordinated fashion incancer development and progression. Cancer Systems Biology is anemerging discipline in which high throughput genomic data andcomputational approaches are integrated to provide a coherent andsystematic understanding of the diverse pathway dysregulationsresponsible for the presentation of the same cancer phenotype. This newdiscipline promises to transform the practice of medicine from areactive one to a predictive one.

High-grade gliomas are the most common form of brain cancer, or braintumors in human beings. Brain tumors are treated similarly to otherforms of tumors with surgery, chemotherapy, and radiation therapy. Thereare relatively few specific drugs that selectively target tumors, andfewer still that target brain tumors. Here is described a pair of genesthat appear to be responsible for the development of high-grade gliomasin humans. This pair of genes, Stat3 and C/EBPβ, can be used in adiagnostic, and serve as potential drug targets for the treatment ofhigh-grade gliomas.

SUMMARY OF THE INVENTION

An aspect of the invention provides a method for treating nervous systemcancer in a subject in need thereof comprising administering to thesubject a compound that inhibitis aMesenchymal-Gene-Expression-Signature (MGES) protein.

An aspect of the invention provides a method for decreasing MGES proteinactivity in a subject having a nervous system cancer, the methodcomprising administering to the subject a compound that inhibits a MGESprotein.

An aspect of the invention provides a method for inhibiting a MGESprotein comprising contacting said protein with an effective amount of aMGES inhibitor compound.

An aspect of the invention provides a method for inhibiting tumor growthcomprising contacting said protein with an effective amount of a MGESinhibitor compound.

An aspect of the invention provides a method for inhibiting cellproliferation comprising contacting said protein with an effectiveamount of a MGES inhibitor compound.

An aspect of the invention provides a method for detecting the presenceof or a predisposition to a nervous system cancer in a human subject. Insome embodiments, the method comprises (a) obtaining a biological samplefrom a subject; and (b) detecting whether or not there is an alterationin the expression of a Mesenchymal-Gene-Expression-Signature (MGES) genein the subject as compared to a subject not afflicted with a nervoussystem cancer. In some embodiments, the MGES gene comprises Stat3,C/EBPβ, C/EBδ, RunX1, FosL2, bHLH-B2, ZNF238, or a combination thereof.In some embodiments, the detecting comprises detecting in the samplewhether there is an increase in a MGES mRNA, a MGES polypeptide, or acombination thereof. In some embodiments some embodiments, the MGES genecomprises Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or a combinationthereof. In some embodiments, the detecting comprises detecting in thesample whether there is a decrease in a MGES mRNA, a MGES polypeptide,or a combination thereof. In some embodiments, the MGES gene comprisesZNF238. In some embodiments, the nervous system cancer comprises aglioma while in other embodiments, the glioma comprises an astrocytoma,a Glioblastoma Multiforme, an oligodendroglioma, an ependymoma, or acombination thereof.

An aspect of the invention provides a method for inhibitingproliferation of a nervous system tumor cell or for promotingdifferentiation of a nervous system tumor cell. In some embodiments, themethod comprises decreasing the expression of aMesenchymal-Gene-Expression-Signature (MGES) molecule in a nervoussystem tumor cell, thereby inhibiting proliferation or promotingdifferentiation. In some embodiments, the proliferation comprises cellinvasion, cell migration, or a combination thereof. In some embodiments,the method comprises treatment of a subject in need thereof with acompound or composition that modulates MGES activity.

An aspect of the invention provides a method for inhibiting angiogenesisin a nervous system tumor, comprising administering to the subject aneffective amount of a compound or composition. In some embodiments, themethod comprises decreasing the expression of aMesenchymal-Gene-Expression-Signature (MGES) molecule in a nervoussystem tumor cell, thereby inhibiting angiogenesis. In some embodiments,the method comprises treatment of a subject in need thereof with acompound or composition that modulates MGES activity.

Another aspect of the invention provides a method for treating a nervoussystem tumor in a subject, comprising administering to the subject aneffective amount of a compound or composition that decreases theexpression of a Mesenchymal-Gene-Expression-Signature (MGES) molecule ina nervous system tumor cell, thereby treating nervous system tumor inthe subject. In some embodiments, the composition is administered to anervous system tumor cell.

Another aspect of the invention provides a method for inhibition of anMGES protein in a subject, comprising administering to the subject aneffective amount of a compound or composition that inhibits the activityof a MGES protein.

An aspect of the invention also provides a method for identifying acompound that binds to a Mesenchymal-Gene-Expression-Signature (MGES)protein. In some embodiments, the method comprises a) providing anelectronic library of test compounds; b) providing atomic coordinatesfor at least 20 amino acid residues for the binding pocket of the MGESprotein, wherein the coordinates have a root mean square deviationtherefrom, with respect to at least 50% of Cα atoms, of not greater thanabout 5 Å, in a computer readable format; c) converting the atomiccoordinates into electrical signals readable by a computer processor togenerate a three dimensional model of the MGES protein; d) performing adata processing method, wherein electronic test compounds from thelibrary are superimposed upon the three dimensional model of the MGESprotein; and e) determining which test compound fits into the bindingpocket of the three dimensional model of the MGES protein, therebyidentifying which compound binds to theMesenchymal-Gene-Expression-Signature (MGES) protein. In someembodiments, the method further comprises f) obtaining or synthesizingthe compound determined to bind to theMesenchymal-Gene-Expression-Signature (MGES) protein or to modulate MGESprotein activity; g) contacting the MGES protein with the compound undera condition suitable for binding; and h) determining whether thecompound modulates MGES protein activity using a diagnostic assay. Insome embodiments, the MGES protein comprises Stat3, C/EBPβ, C/EBPδ,RunX1, FosL2, bHLH-B2, ZNF238. In some embodiments, the compound is aMGES antagonist or MGES agonist. In some embodiments, the antagonistdecreases MGES protein or RNA expression or MGES activity by at leastabout 10%, at least about 20%, at least about 30%, at least about 40%,at least about 50%, at least about 60%, at least about 70%, at leastabout 75%, at least about 80%, at least about 90%, at least about 95%,at least about 99%, or 100%. In some embodiments, the antagonist isdirected to Stat3, C/EBIβ, C/EBPδ, RunX1, FosL2, bHLH-B2 or acombination thereof. In some embodiments, the agonist increases MGESprotein or RNA expression or MGES activity by at least about 10%, atleast about 20%, at least about 30%, at least about 40%, at least about50%, at least about 60%, at least about 70%, at least about 75%, atleast about 80%, at least about 90%, at least about 95%, at least about99%, or 100%. In some embodiments, the agonist is directed to ZNF238.

An aspect of the invention further provides for a compound identified bythe screening method discussed herein, wherein the compound binds toMGES. In some embodiments, the compound binds to the active site ofMGES.

An aspect of the invention also provides a method for decreasing MGESgene expression in a subject having a nervous system cancer, wherein themethod comprises administering to the subject an effective amount of acomposition comprising a MGES inhibitor compound, thereby decreasingMGES expression in the subject. In some embodiments, the compositioncomprises an MGES modulator compound. In some embodiments, the compoundcomprises an antibody that specifically binds to a MGES protein or afragment thereof; an antisense RNA or antisense DNA that inhibitsexpression of MGES polypeptide; a siRNA that specifically targets a MGESgene; a shRNA that specifically targets a MGES gene; or a combinationthereof.

An aspect of the invention further provides for a diagnostic kit fordetermining whether a sample from a subject exhibits increased ordecreased expression of at least 2 or more MGES genes (e.g., Stat3,C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238), the kit comprisingnucleic acid primers that specifically hybridize to an MGES gene,wherein the primer will prime a polymerase reaction only when a nucleicacid sequence comprising any one of SEQ ID NOS: 232, 234, 236, 238, 240,242, or 244 is present.

In some embodiments, the compound is selected from the group consistingof etoposide, 5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the composition, comprises a compound selected fromthe group consisting of etoposide, 5-fluorouracil, Clostridium difficileToxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the MGES protein is C/EPB or Stat3. In someembodiments, the MGES protein is C/EPB. In some embodiments, the MGESprotein is Stat3.

In some embodiments, the cancer is glioma or meningioma. In someembodiments, the cancer is astrocytoma, Glioblastoma Multiforme,oligodentroglioma, ependymoma or meningioma. In some embodiments, thecancer is cerebellar astrocytoma, medulloblastoma, ependymona, brainstem glioma, optic nerve glioma, acoustic neuromas, nerve sheath tumors,or germinoma.

These and other embodiments of the invention are further described inthe following sections of the application, including the DetailedDescription, Examples, and Claims. Still other objects and advantages ofthe invention will become apparent by those of skill in the art from thedisclosure herein, which are simply illustrative and not restrictive.Thus, other embodiments will be recognized by the ordinarily skilledartisan without departing from the spirit and scope of the invention.

BRIEF DESCRIPTION OF THE FIGURES

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

FIG. 1 is a schematic depicting the mesenchymal subnetwork of six majorhubs of transcription factors (TFs) in high-grade gliomas whichrepresents the mesenchymal signature of high-grade gliomas is controlledby six TFs. The TFs positively (pink) or negatively (blue) linked asfirst neighbors to the mesenchymal genes of human gliomas (green)connect 74% of the genes composing the MGES. The six TF control 74% ofthe genes in the mesenchymal signature of high-grade glioma.

FIG. 2 is a photographic representation of a blot showing expression ofthe TFs connected with the MGES in primary GBM. Semiquantitative RT-PCRwas performed in 17 GBM samples, in the SNB75 glioblastoma cell line andnormal brain. 18S RNA was used as control.

FIG. 3 shows the validation of direct targets of the TFs connected withthe MGES by ChIP analysis. A region between 2 kb upstream and downstreamthe transcription start of the targets identified with ARACNe wasanalyzed for the presence of putative binding sites. Genomic regions ofgenes containing putative binding sites for specific TFs wereimmunoprecipitated in the SNB75 cell line by antibodies specific forStat3 (FIG. 3A), C/EBPβ (FIG. 3B), FosL2 (FIG. 3C), and bHLH-B2 (FIG.3D). SOCS3 was included as positive control of Stat3 binding. Totalchromatin before immunoprecipitation (input DNA) was used as positivecontrol for PCR. The OLR1 gene was used as a negative control. FIG. 3Eshows the summary of binding results of the tested TFs to mesenchymaltargets.

FIG. 4 shows a combinatorial and hierarchical module directsinteractions between the master mesenchymal TFs. The promoters of theTFs connected to the MGES were analyzed for the presence of putativebinding sites for Stat3 (FIG. 4A), C/EBPβ (FIG. 4B), FosL2 (FIG. 4C),and bHLHB2 (FIG. 4D) through the MatInspector software (Genomatix)followed by ChIP. FIG. 4E shows a graphical representation of thetranscriptional network emerging from promoter occupancy analysis,including autoregulatory and feed-forward loops among TFs. FIG. 4F showsquantitative RT-PCR analysis of mesenchymal TFs in GBM-BTSCs infectedwith lentivirus expressing Stat3/C/EBPβ shRNA. Gene expression isnormalized to the expression of 18S ribosomal RNA.

FIG. 5A shows photographic images of the morphology of Stat3 plusC/EBPβ-expressing clones grown in the presence and absence of mitogens.Ectopic Stat3C and C/EBPβ in NSCs induce a mesenchymal phenotype,enhance migration and invasion and inhibit proneural gene expression.

FIG. 5B shows Gene Set Enrichment Analysis plots. Following ectopicexpression of C/EBPβ and Stat3 in NCSs, mesenchymal (mes) andproliferative (prolif) genes were highly enriched among upregulatedgenes, while the proneural (PN) genes were highly enriched amongdown-regulated genes. Top portion of the graph shows the enrichmentscore profile. The maximum (minimum) value of this curve determines theenrichment score among up-regulated (down-regulated) genes. Middleportion of the graph shows the signature genes as black vertical bars.The bottom portion shows the weight of each ranked gene (proportional toits statistical significance). The figure is separated into two pages,joining at the hatched line.

FIG. 5C are microphotographs of C17.2 expressing Stat3C and C/EBPβ orthe empty vector. 1 mm scratch was made with a pipette tip on confluentcultures (upper panels): The ability of the cells to cover the scratchwas evaluated after three days (lower panels). *p≦0.05, **p≦0.01.

FIG. 5D shows microphotographs of invading C17.2 cells expressing Stat3Cand C/EBPβ or transduced with empty vector (upper panels).Quantification of cell invasion in the absence or in the presence ofPDGF. Bars indicate Mean±SEM of triplicate samples. *p≦0.05, **p≦0.01.

FIG. 6 depicts that neural stem cells expressing Stat3C and C/EBPβacquire tumorigenic capability in vivo. FIG. 6A shows six-week oldBALBc/nude mice that were injected subcutaneously with C17.2-vector(left flank) or C17.2 expressing Stat3C plus C/EBPβ (right flank). Thenumber of tumors observed is indicated in the table. Mice weresacrificed 10 weeks (5×10⁶ cells) or 13 weeks (2.5×10⁶ cells) afterinjection. Black arrows point to the normal appearance of the left flankinjected with CTR cells. White arrows point to the tumor mass in theright flank injected with C17.2 expressing Stat3C plus C/EBPβ. FIG. 6Bare photographs of Hematoxylin & Eosin staining of two representativetumors depicting areas of pleomorphic cells forming pseudopalisades(upper panels; Inset: N, necrosis) and intensive network of aberrantvascularization (lower panels). FIG. 6C are photographic microscopyimages of tumors that exhibit immunopositive areas for the proliferationmarker Ki67, the progenitor marker Nestin, and diffuse staining for thevascular endothelium as evaluated by CD31. FIG. 6D are photographicmicroscopy images of tumors that display mesenchymal markers asindicated by positive immunostaining for OSMR and FGFR-1. Tworepresentative tumors are shown.

FIGS. 7A-7B show expression of Stat3 and C/EBPβ is essential for themesenchymal phenotype of human glioma. FIG. 7A is a photographic imageof a western blot of Stat3 and C/EBPβ in brain tumor stem cells (BTSCs)transduced with lentivirus CTR or expressing Stat3 and C/EBPβ shRNA.FIG. 7B is a graphic representation of the GSEA plot for the mesenchymalgenes.

FIG. 7C is a bar graph that shows quantitative RT-PCR of mesenchymalgenes in BTSCs infected with lentiviruses expressing Stat3/C/EBPβ shRNA.Gene expression is normalized to the expression of 18S rRNA.

FIG. 7D is a graphic representation of a GSEA plot. The MGES isdownregulated in SNB19 cells infected with shStat3 plus shC/EBPβsilencing lentiviruses.

FIG. 7E shows photographic images of invading SNB19 cells infected withshStat3 plus shC/EBPβ lentiviruses. The graph shows Mean+/−SD of twoindependent experiments, each performed in triplicate.

FIG. 7F is a graph depicting Kaplan-Meier survival of patients carryingtumors positive for Stat3 and C/EBPβ (double positives, red line) anddouble/single negative tumors (black line).

FIG. 8 depicts that MINDy-inferred STK38 is a post-translationalmodulator of MYC. (FIG. 8A) rows represent MYC targets, columnsrepresent distinct samples. Expression is color coded from blue(underexpressed) to red (overexpressed) with respect to the mean acrossall experiments. MYC ability to transcriptionally regulate its targetsis reduced in samples with lower STK38 expression. Silencing of STK38leads to reduction in MYC protein (FIG. 8B), consistent changes invalidated MYC targets (FIG. 8C), but no change in MYC mRNA (FIG. 8C)

FIG. 9 is a graph that shows the expression of ZNF238 is significantlydown-regulated in 77 samples from human GBM (class 2, red) compared with23 samples from non-tumor human brains (class 1, blue). P-value: 6.8E-5.

FIG. 10 is a graph that shows expression of ZNF238 in tumors derivedfrom NCS expressing Stat3/C/EBPβ. RNA was prepared from cells beforeinjection and two representative tumors. Quantitative RT-PCR wasperformed using 18S as internal control.

FIG. 11 is a bar graph that shows SiRNA-mediated silencing of ZNF238 inNSCs expressing Stat3 and C/EBPβupregulates the expression ofmesenchymal genes.

FIG. 12 shows graphs that depict results from epigenetic silencing ofZNF238 in malignant glioma cells. FIG. 12A, Graphical representation ofthe promoter of ZNF238. The region between −1800 and −3400 containsstretches of CpG islands. FIG. 12B, 5-Azacytidine induces expression ofZNF238. T98G cells were treated with 5-Azacytidine at the indicatedconcentrations for 3 days. Expression of ZNF238 was analyzed byquantitative PCR. FIG. 12C, Expression of selected ZNF238 targets isdown-regulated after treatment with 5-Azacytidine. HPRT was used ascontrol for normalization.

FIG. 13 is a schematic for the generation of mice carrying conditionalinactivation of the ZNF238 gene. A 10.3 Kb genomic fragment containingZNF238 locus has been retrieved into PL253 plasmid by recombineeringusing the recombination proficient bacterial strain SW102, whichexpresses the recombinase components exo, bet, and gam. A loxP site willbe introduced in intron 1, upstream of the ZNF238 coding region. AloxP-flanked Neo-STOP cassette (LSL) from pBS302 vector will beintroduced into the 3′ untranslated region of exon 2 by recombineering.The LSL cassette was obtained from Tyler Jacks. The linearized targetingvector will be introduced into ES cells by electroporation. Deletion ofthe coding region in exon 2 by Cre in vivo will generate ZNF238-nullmice.

FIG. 14 depicts GEP profiles from the Glioma Connectivity Map will beused to prioritize candidate druggable targets for MGES inhibition. Foreach Candidate Pharmacological Target (CPT), samples will be sorted byCPT expression. Enrichment of the MGES in genes that are differentiallyexpressed in the GEPs that express the highest/lowest CPT levels will beused to assess the likelihood that the CPT is effective in suppressingthe MGES.

FIG. 15 is a fluorescent photographic image depicting the silencing ofStat3 and C/EBPβ in human GBM-BTSCs induces apoptosis. Cells transducedwith sh-CTR or sh-Stat3 plus sh-C/EBPβ. Cells were immunostained forCaspase3. Nuclei were counterstained with DAPi.

FIG. 16 is a photograph of a blot showing chromatin immunoprecipitationfor Stat3 (FIG. 16A) and C/EBPβ (FIG. 16B) from a primary GBM sample.

FIG. 17 shows that ectopic expression of C/EBPβ and Stat3C cooperativelyinduce the expression of mesenchymal markers in NSCs. FIG. 17A is aphotographic image of a western blot. FIG. 17B shows Immunofluorescencestaining for SMA (upper panel) and fibronectin (lower panel) in C17.2expressing the indicated TFs. FIG. 17C depicts the quantification of SMApositive cells (upper panel). For fibronectin immunostaining theintensity of fluorescence was quantified (lower panel). Bars indicateMean±SD. n=3 for each group. **p≦0.01, ***p≦0.001. FIG. 17D-G shows theQRT-PCR analysis of mesenchymal targets in C17.2 expressing theindicated TFs or transduced with the empty vector. Gene expression wasnormalized to the expression of 18S ribosomal RNA. Bars indicateMean±SD. n=3 for each group. **p≦0.01, ***p≦0.001.

FIG. 18 shows that C/EBPβ and Stat3 inhibit neural differentiation ofNSCs, induce mesenchymal transformation and promote invasiveness. FIG.18A is a photographic image of a semi-quantitative RT-PCR analysis ofmesenchymal and neural markers in C17.2 expressing Stat3C plus C/EBPβ orcontrol vector cultured in growth medium (E) or after removal ofmitogens for 5 or 10 days. FIG. 18B are microscope photographs of Alcianblue staining of C17.2 expressing Stat3C and C/EBPβ, or transduced withempty vector cultured in growth medium (upper panels), or inchondrogenesis differentiation medium for 20 days (lower panels).

FIG. 19 shows that C/EBPβ and Stat3 inhibit neural differentiation andtrigger mesenchymal transformation of primary mouse NSCs. FIG. 19A arephotomicrographs of immunofluorescence staining for CTGF in primary NSCstransduced with retroviruses expressing Stat3C and C/EBPβ or the emptyvector. GFP identifies the infected cells. FIG. 19B is a graph showingthe quantification of GFP positive/CTGF positive cells. Bars indicateMean±SD of three independent experiments. **p≦0.01. FIG. 19C is a graphshowing QRT-PCR of mesenchymal genes in primary N, SCs transduced withStat3C, C/EBPβ, Stat3C plus C/EBPβ, or empty vectors. Bars indicateMean±SD of 3 independent reactions. Gene expression was normalized tothe expression of 18S ribosomal RNA. FIGS. 19D-F are graphs showingQRT-PCR of neuronal (βIII-tubulin and doublecortin) and glial (GFAP)markers in primary NSCs transduced with Stat3C plus C/EBPβ, or withempty retroviruses. Cells were grown for 5 days in the presence orabsence of mitogens. Bars indicate Mean±SD of three independentreactions. Gene expression was normalized to the expression of 18Sribosomal RNA.

FIG. 20 shows that C/EBPβ and Stat3 are essential to maintain themesenchymal phenotype of human glioma cells. FIG. 20A aremicrophotographs of immunofluorescence for fibronectin, Col5A1 and YKL40in BTSC-3408 infected with lentiviruses expressing Stat3, C/EBPβ, orStat3 plus C/EBPβ shRNA. Nuclei were counterstained with DAPI.Quantification of fibronectin (FIG. 20C), Col5A1 (FIG. 20D) and YKL40(FIG. 20E) positive cells from the representative experiment shown in(FIG. 20A). Bars indicate Mean±SD of 3 independent experiments. *p≦0.05,**p≦0.01, ***p≦0.001. FIG. 20B are photomicrographs ofimmunofluorescence for Col5A1 and YKL40 in SNB19 cells infected as inFIG. 20A. Quantification of Col5A1 (FIG. 20F) and YKL40 (FIG. 20G)positive cells in experiments in (FIG. 20B). Bars indicate Mean±SD of 3independent experiments. *p≦0.05, **p≦0.01. QRT-PCR of mesenchymal genesin BTSC-20 (FIG. 20H), BTSC-3408 (FIG. 20I) and SNB19 (FIG. 20J)infected with lentiviruses expressing Stat3, C/EBPβ, or Stat3 plusC/EBPβ shRNA. Bars indicate Mean±SD of three independent reactions. FIG.20K is a bar graph showing the quantification of Stat3 plus C/EBPβshRNA.

FIG. 21 shows that knockdown of C/EBPβ and Stat3 impairs tumorformation, invasion and expression of mesenchymal markers in a mousemodel of human SNB19 glioma. FIG. 21A depicts a Kaplan-Meier survivalcurve of NOD SCID mice transplanted intracranially with SNB19 gliomacells that had been transduced with shCtr (red), shStat3 (black),shC/EBPβ (green) or shStat3 plus shC/EBPβ (blue) lentiviruses. **p≦0.01.Immunofluorescence staining for human Vimentin (FIG. 21B), CD31 (FIG.21C), fibronectin (FIG. 21D), Col5A1 (FIG. 21E) and YKL40 (FIG. 21F) oftumors derived from SNB19 cells infected with lentiviruses expressingshRNA targeting Stat3, C/EBPβ, or Stat3 plus C/EBPβ. T, tumor; B, normalbrain.

FIG. 22 shows that C/EBPβ and Stat3 are essential for glioma tumoraggressiveness in mice and humans. FIG. 22A depicts invading BTSC-3408cells infected with shCtr, shStat3, shC/EBPβ or shStat3 plus shC/EBPβlentiviruses and the quantification of invading cells (graph below).Bars indicate Mean±SD of two independent experiments, each performed intriplicate (right panel). *p≦0.01. FIG. 22B shows immunostaining forhuman vimentin (left panels) on representative brain sections from miceinjected with BTSC-3408 after silencing of C/EBPβ and Stat3.Quantification of human vimentin positive area (right panel). FIG. 22Cshows immunostaining for Ki67 from tumors as in FIG. 22B (left panels).Quantification of Ki67 positive cells (right panel). Bars indicateMean±SD. n=5 for each group. *p≦0.05. (St, striatum; CC, corpuscallosum). Immunostaining for fibronectin (FIG. 22D) and Col5A1 (FIG.22E) on representative brain sections from mice injected with BTSC-3408that had been transduced treated as indicated. Nuclei werecounterstained with DAPI. f, Kaplan-Meier analysis comparing survival ofpatients carrying tumors positive for C/EBPβ and Stat3 (doublepositives, red line) and double/single negative tumors (black line).

FIG. 23 is a schematic that shows altered MGES gene expression does notresult from copy number changes. The correlation between gene expressionand DNA copy number for the MGES genes was determined using data from 76high-grade gliomas for which both gene expression array (AffymetrixU133A) and array comparative genomic hybridization (aCGH) profiling hasbeen performed as previously described{Phillips, 2006 #1049}. Tumorswere grouped based on molecular subtype (proneural, mesenchymal, orproliferative) and the mean expression of each MGES gene determined.Genes are shown in order of increasing mean expression. The normalizedcopy number (error bars indicate standard deviation) of each gene wasinterpolated based on the copy number of the nearest genomic clone onthe CGH array as determined by comparison of the sequence annotation ofboth array platforms. No correlation was seen between the mean MGES geneexpression and DNA copy number for the proneural, mesenchymal,proliferative groups or the total cohort (p=0.09430, 0.1058, 0.09430,0.1014, respectively; Spearman's rho).

FIG. 24 are graphs that show the correlation between microarray andQRT-PCR measures for Stat3 (FIG. 24A) and C/EBPβ (FIG. 24B) mRNAs. Shownis the ratio of mRNA levels for C/EBPβ and Stat3 between silencing orover-expression and the corresponding non-targeting shRNA or vectorcontrols, respectively. QRT-PCR estimates α-axis) are in log₁₀ scale,and microarray estimates (y-axis) are in log₂ scale.

FIG. 25 is a graph of GSEA analysis that confirmed that MGES genes weremarkedly enriched in the TWPS signature. The bar-code plot indicates theposition of the MGES genes on the TCGA expression data rank-sorted byits association with bad prognosis, red and blue colors indicatepositive and negative differential expression, respectively. The grayscale bar indicates the t-statistic values, used as weighting score forGSEA analysis.

FIG. 26 shows ectopic Stat3C and C/EBPβ in NSCs induce a mesenchymalphenotype and inhibit neuronal differentiation. FIG. 26A showsimmunofluorescence for Tau and SMA in two C17.2 subclones expressingStat3C and C/EBP or control vector cultured in absence of mitogens for10 days. Nuclei were counterstained with DAPI. FIG. 26B aremicrophotographs of primary mouse NSCs expressing Stat3C and C/EBPβ orcontrol vector grown in absence of growth factors. Note thedifferentiated cells with neuronal-like morphology in the control cells.

FIG. 27 are photomicrographs that show YKL-40 expression correlates withC/EBPβ and Stat3 expression in primary tumors. Immunohistochemistryanalysis of YKL-40, C/EBPβ and Stat3 expression in tumors from patientswith newly diagnosed GBM. FIG. 27A shows a representativeYKL-40/Stat3C/EBPβ-triple positive tumor. FIG. 27B shows arepresentative YKL-40/Stat3/C/EBPβ-triple negative tumor.

FIG. 28. is a graph showing change in gene expression.

FIG. 29 is a schematic that shows the top 50 genes downregulated (FIG.29A) and the top 50 genes downregulated (FIG. 29B).

FIG. 30 shows chromatin immunoprecipitation for Stat3 and C/EBPβ (FIG.30A) from primary GBM tumor samples and quantitation of their expression(FIG. 30B).

FIG. 31A is a venn-diagram that depicts the proportion of mesenchymalgenes identified by ARACNe as targets of only C/EBPβ, STAT3 or both TFs.

FIG. 31B is a heatmap of MGES gene expression analysis of mouse andhuman cells carrying perturbations of C/EBPβ plus STAT3. Samples(columns) were grouped according to species and treatment. Control,control shRNA or empty vector; S−, STAT3 knockdown; S+, STAT3overexpression; C−, CEBPB knockdown; C+, CEBPB overexpression; S−/C−,STAT3 and CEBPB knockdown; S+/C+, STAT3 and CEBPB overexpression.

FIG. 32 is a graph showing the GSEA of the MGES on the gene expressionprofile rank-sorted according to the correlation with the CEBPB×STAT3metagene. The bar-code plot indicates the position of MGES genes, lightgray (right hand side) and dark grey (left hand side) colors representpositive and negative correlation, respectively. The grey scale barindicates the Spearman's rho coefficient used as weighting score forGSEA. LEOR, leading-edge odds ratio; nES, normalized enrichment score;P, sample-permutation-based P value

FIG. 33 is a schematic diagram of the experimental strategy used toidentify and experimentally validate the transcription factors (TFs)that drive the mesenchymal phenotype of malignant glioma.Reverse-engineering of a high grade glioma-specific mesenchymalsignature reveal the transcriptional regulatory module that activatesexpression of the mesenchymal genes. Two transcription factors (C/EBPβand STAT3) emerge as synergistic master regulators of mesenchymaltransformation. Elimination of the two factors in glioma cells leads tocollapse of the mesenchymal signature and reduces tumor formation andaggressiveness in the mouse. In human glioma, the combined expression ofC/EBPβ and STAT3 is a strong predicting factor for poor clinicaloutcome.

FIG. 34 shows that mesenchymal genes are coordinately regulated byC/EBPβ and Stat3. Gene expression integrative analysis of mouse andhuman cells carrying perturbations of C/EBPβ (FIG. 34A) and Stat3 (FIG.34B). Heatmaps represent mRNA levels for MGES genes. Genes are in rowsand samples in columns. The 89 profiled samples were grouped accordingto species and treatment: control shRNA or empty vector (Control), Stat3knock-down (S−), Stat3 overexpression (S+), C/EBPβ knock-down (C−),C/EBPβ overexpressoin (C+), simultaneous knockdown or over-expression ofboth TFs (S−/C− and S+/C+). The first row of each heatmap shows the mRNAlevels of C/EBPβ and Stat3 as assessed by qRT-PCR. Genes were sortedaccording to the Spearman correlation with the mRNA levels of thespecific TF being tested. Dark grey and light gray intensity indicatelower and higher expression levels than the gene expression median,respectively. Leading edge mesenchymal genes are above the horizontalblack line. GSEA analysis of the MGES on the gene expression profilerank-sorted is shown according to the correlation with C/EBPβ (FIG. 34C)and Stat3 (FIG. 34D). The bar-code plot indicates the position of theMGES genes, dark gray (left-hand side of the plot) and light gray(right-hand side of the plot) colors indicate positive and negativecorrelation, respectively. The gray scale bar indicates the spearman rhocoefficient, used as weighting score for GSEA analysis. nES, normalizedenrichment score; p, sample-permutation-based p-value.

FIG. 35 shows results from C/EBPβ and STAT3 luciferase reporter assays.TRANSIENT analysis of the reporters is shown in the bar graphs, LeftPanel (STAT3, Top; and C/EBPβ, Bottom) and in the blots of expression,Middle Panel (STAT3, Top; and C/EBPβ, Bottom). A schematic of luciferasereporter vectors expressing STAT3 (Top) and C/EBPβ (Bottom) are shown inthe right panel.

FIG. 36 shows expression levels of SNB19 human glioma cell clones thatwere stably transfected with the C/EBPbeta-driven luciferase plasmid andsubsequently transfected with control siRNAs or siRNA oligonucleotidestargeting C/EBPbeta.

FIG. 37 shows expression levels of SNB19 human glioma cell clones thatwere stably transfected with the C/EBPbeta-driven luciferase plasmid andsubsequently transfected with control siRNAs or two different siRNAoligonucleotides targeting C/EBPbeta (siCEBPb05 and siCEBP06).

FIG. 38 shows inhibition using a C/EBPb gene reporter assay. FIG. 38Ashows CEBPb reporter activity at 48 hr upon inhibition with variousdosages of 5-fluorouracil (5-FU). FIG. 38B shows ATP cell viability at24 hr and 48 hr upon inhibition with various dosages of 5-FU.

FIG. 39 shows inhibition using a C/EBPb gene reporter assay. FIG. 39Ashows CEBPb reporter activity at 48 hr upon inhibition with variousdosages of clostridium difficilis Toxin B (CD Toxin B). FIG. 39B showsATP cell viability at 24 hr and 48 hr upon inhibition with variousdosages of CD Toxin B.

DETAILED DESCRIPTION OF THE INVENTION

Key features of nervous system cancer progression are relentlessproliferation, loss of differentiation and angiogenesis (Iavarone andLasorella, 2004. Cancer Letters 204: 189-96; herein incorporated byreference in its entirety). Here, the invention is directed totranscriptional modules that can synergistically initiate and maintainmesenchymal transformation in the brain. For example, the invention isdirected to regulating the mesenchymal state of brain cells, a signatureof human glioma. In some embodiments, transcription factors thatcomprise a transcriptional module involved in the synergistic regulationof the mesenchymal signature of malignant glioma (Mesenchymal GeneExpression Signature of high-grade glioma (MGES)) are regulated so as toreduce nervous system cancers. MGES genes can include, but are notlimited to, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238,or a combination thereof. In some embodiments, the protein or mRNAexpression levels of Stat3 and/or C/EBPβ can be decreased in order toameliorate glioma cancers. For example, silencing of the twotranscription factors depletes glioma stem cells and cell lines ofmesenchymal attributes and greatly impairs their ability to invade.

The invention is also directed methods of inducing spinal axonregeneration by way of a stabilized Id2 composition. In someembodiments, the delivery of Adeno-Associated Viruses encodingundegradable Id2 (Id2-DBM) can promote axonal regeneration andfunctional locomotor recovery in a mouse model of hemisection spinalcord injury.

As used herein, “Mesenchymal Gene Expression Signature” or “MGES” refersto a transcription factor that comprises a transcriptional moduleinvolved in the synergistic regulation of the mesenchymal signature ofmalignant glioma or high-grade glioma. For example, MGES genes caninclude, but are not limited to, Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238. MGES proteins can be polypeptides encoded by aStat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238 nucleotidesequence.

The polypeptide sequence of human signal transducer and activator oftranscription 3 (STAT3) is depicted in SEQ ID NO: 231. The nucleotidesequence of human STAT3 is shown in SEQ ID NO: 232. Sequence informationrelated to STAT3 is accessible in public databases by GenBank Accessionnumbers NM_(—)139276 (for mRNA) and NP_(—)644805 (for protein).

SEQ ID NO: 231 is the human wild type amino acid sequence correspondingto STAT3 (residues 1-769), wherein the bolded sequence represents themature peptide sequence:

1 MAQWNQLQQL DTRYLEQLHQ LYSDSFPMEL RQFLAPWIES QDWAYAASKE SHATLVFHNL 61LGEIDQQYSR FLQESNVLYQ HNLRRIKQFL QSRYLEKPME IARIVARCLW EESRLLQTAA 121TAAQQGGQAN HPTAAVVTEK QQMLEQHLQD VRKRVQDLEQ KMKVVENLQD DFDFNYKTLK 181SQGDMQDLNG NNQSVTRQKM QQLEQMLTAL DQMRRSIVSE LAGLLSAMEY VQKTLTDEEL 241ADWKRRQQIA CIGGPPNICL DRLENWITSL AESQLQTRQQ IKKLEELQQK VSYKGDPIVQ 301HRPMLEERIV ELFRNLMKSA FVVERQPCMP MHPDRPLVIK TGVQFTTKVR LLVKFPELNY 361QLKIKVCIDK DSGDVAALRG SRKFNILGTN TKVMNMEESN NGSLSAEFKH LTLREQRCGN 421GGRANCDASL IVTEELHLIT FETEVYHQGL KIDLETHSLP VVVISNICQM PNAWASILWY 481NMLTNNPKNV NFFTKPPIGT WDQVAEVLSW QFSSTTKRGL SIEQLTTLAE KLLGPGVNYS 541GCQITWAKFC KENMAGKGFS FWVWLDNIID LVKKYILALW NEGYIMGFIS KERERAILST 601KPPGTFLLRF SESSKEGGVT FTWVEKDISG KTQIQSVEPY TKQQLNNMSF AEIIMGYKIM 661DATNILVSPL VYLYPDIPKE EAFGKYCRPE SQEHPEADPG AAPYLKTKFI CVTPTTCSNT 721IDLPMSPRTL DSLMQFGNNG EGAEPSAGGQ FESLTFDMEL TSECATSPM

SEQ ID NO: 232 is the human wild type nucleotide sequence correspondingto STAT3 (nucleotides 1-4978), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 ggtttccgga gctgcggcgg cgcagactgg gagggggagc cgggggttcc gacgtcgcag 61ccgagggaac aagccccaac cggatcctgg acaggcaccc cggcttggcg ctgtctctcc 121ccctcggctc ggagaggccc ttcggcctga gggagcctcg ccgcccgtcc ccggcacacg 181cgcagccccg gcctctcggc ctctgccgga gaaacagttg ggacccctga ttttagcagg 241atg gcccaat ggaatcagct acagcagctt gacacacggt acctggagca gctccatcag 301ctctacagtg acagcttccc aatggagctg cggcagtttc tggccccttg gattgagagt 361caagattggg catatgcggc cagcaaagaa tcacatgcca ctttggtgtt tcataatctc 421ctgggagaga ttgaccagca gtatagccgc ttcctgcaag agtcgaatgt tctctatcag 481cacaatctac gaagaatcaa gcagtttctt cagagcaggt atcttgagaa gccaatggag 541attgcccgga ttgtggcccg gtgcctgtgg gaagaatcac gccttctaca gactgcagcc 601actgcggccc agcaaggggg ccaggccaac caccccacag cagccgtggt gacggagaag 661cagcagatgc tggagcagca ccttcaggat gtccggaaga gagtgcagga tctagaacag 721aaaatgaaag tggtagagaa tctccaggat gactttgatt tcaactataa aaccctcaag 781agtcaaggag acatgcaaga tctgaatgga aacaaccagt cagtgaccag gcagaagatg 841cagcagctgg aacagatgct cactgcgctg gaccagatgc ggagaagcat cgtgagtgag 901ctggcggggc ttttgtcagc gatggagtac gtgcagaaaa ctctcacgga cgaggagctg 961gctgactgga agaggcggca acagattgcc tgcattggag gcccgcccaa catctgccta 1021gatcggctag aaaactggat aacgtcatta gcagaatctc aacttcagac ccgtcaacaa 1081attaagaaac tggaggagtt gcagcaaaaa gtttcctaca aaggggaccc cattgtacag 1141caccggccga tgctggagga gagaatcgtg gagctgttta gaaacttaat gaaaagtgcc 1201tttgtggtgg agcggcagcc ctgcatgccc atgcatcctg accggcccct cgtcatcaag 1261accggcgtcc agttcactac taaagtcagg ttgctggtca aattccctga gttgaattat 1321cagcttaaaa ttaaagtgtg cattgacaaa gactctgggg acgttgcagc tctcagagga 1381tcccggaaat ttaacattct gggcacaaac acaaaagtga tgaacatgga agaatccaac 1441aacggcagcc tctctgcaga attcaaacac ttgaccctga gggagcagag atgtgggaat 1501gggggccgag ccaattgtga tgcttccctg attgtgactg aggagctgca cctgatcacc 1561tttgagaccg aggtgtatca ccaaggcctc aagattgacc tagagaccca ctccttgcca 1621gttgtggtga tctccaacat ctgtcagatg ccaaatgcct gggcgtccat cctgtggtac 1681aacatgctga ccaacaatcc caagaatgta aactttttta ccaagccccc aattggaacc 1741tgggatcaag tggccgaggt cctgagctgg cagttctcct ccaccaccaa gcgaggactg 1801agcatcgagc agctgactac actggcagag aaactcttgg gacctggtgt gaattattca 1861gggtgtcaga tcacatgggc taaattttgc aaagaaaaca tggctggcaa gggcttctcc 1921ttctgggtct ggctggacaa tatcattgac cttgtgaaaa agtacatcct ggccctttgg 1981aacgaagggt acatcatggg ctttatcagt aaggagcggg agcgggccat cttgagcact 2041aagcctccag gcaccttcct gctaagattc agtgaaagca gcaaagaagg aggcgtcact 2101ttcacttggg tggagaagga catcagcggt aagacccaga tccagtccgt ggaaccatac 2161acaaagcagc agctgaacaa catgtcattt gctgaaatca tcatgggcta taagatcatg 2221gatgctacca atatcctggt gtctccactg gtctatctct atcctgacat tcccaaggag 2281gaggcattcg gaaagtattg tcggccagag agccaggagc atcctgaagc tgacccaggt 2341agcgctgccc catacctgaa gaccaagttt atctgtgtga caccaacgac ctgcagcaat 2401accattgacc tgccgatgtc cccccgcact ttagattcat tgatgcagtt tggaaataat 2461ggtgaaggtg ctgaaccctc agcaggaggg cagtttgagt ccctcacctt tgacatggag 2521ttgacctcgg agtgcgctac ctcccccatg tgaggagctg agaacggaag ctgcagaaag 2581atacgactga ggcgcctacc tgcattctgc cacccctcac acagccaaac cccagatcat 2641ctgaaactac taactttgtg gttccagatt ttttttaatc tcctacttct gctatctttg 2701agcaatctgg gcacttttaa aaatagagaa atgagtgaat gtgggtgatc tgcttttatc 2761taaatgcaaa taaggatgtg ttctctgaga cccatgatca ggggatgtgg cggggggtgg 2821ctagagggag aaaaaggaaa tgtcttgtgt tgttttgttc ccctgccctc ctttctcagc 2881agctttttgt tattgttgtt gttgttctta gacaagtgcc tcctggtgcc tgcggcatcc 2941ttctgcctgt ttctgtaagc aaatgccaca ggccacctat agctacatac tcctggcatt 3001gcacttttta accttgctga catccaaata gaagatagga ctatctaagc cctaggtttc 3061tttttaaatt aagaaataat aacaattaaa gggcaaaaaa cactgtatca gcatagcctt 3121tctgtattta agaaacttaa gcagccgggc atggtggctc acgcctgtaa tcccagcact 3181ttgggaggcc gaggcggatc ataaggtcag gagatcaaga ccatcctggc taacacggtg 3241aaaccccgtc tctactaaaa gtacaaaaaa ttagctgggt gtggtggtgg gcgcctgtag 3301tcccagctac tcgggaggct gaggcaggag aatcgcttga acctgagagg cggaggttgc 3361agtgagccaa aattgcacca ctgcacactg cactccatcc tgggcgacag tctgagactc 3421tgtctcaaaa aaaaaaaaaa aaaaaagaaa cttcagttaa cagcctcctt ggtgctttaa 3481gcattcagct tccttcaggc tggtaattta tataatccct gaaacgggct tcaggtcaaa 3541cccttaagac atctgaagct gcaacctggc ctttggtgtt gaaataggaa ggtttaagga 3601gaatctaagc attttagact tttttttata aatagactta ttttcctttg taatgtattg 3661gccttttagt gagtaaggct gggcagaggg tgcttacaac cttgactccc tttctccctg 3721gacttgatct gctgtttcag aggctaggtt gtttctgtgg gtgccttatc agggctggga 3781tacttctgat tctggcttcc ttcctgcccc accctcccga ccccagtccc cctgatcctg 3841ctagaggcat gtctccttgc gtgtctaaag gtccctcatc ctgtttgttt taggaatcct 3901ggtctcagga cctcatggaa gaagaggggg agagagttac aggttggaca tgatgcacac 3961tatggggccc cagcgacgtg tctggttgag ctcagggaat atggttctta gccagtttct 4021tggtgatatc cagtggcact tgtaatggcg tcttcattca gttcatgcag ggcaaaggct 4081tactgataaa cttgagtctg ccctcgtatg agggtgtata cctggcctcc ctctgaggct 4141ggtgactcct ccctgctggg gccccacagg tgaggcagaa cagctagagg gcctccccgc 4201ctgcccgcct tggctggcta gctcgcctct cctgtgcgta tgggaacacc tagcacgtgc 4261tggatgggct gcctctgact cagaggcatg gccggatttg gcaactcaaa accaccttgc 4321ctcagctgat cagagtttct gtggaattct gtttgttaaa tcaaattagc tggtctctga 4381attaaggggg agacgacctt ctctaagatg aacagggttc gccccagtcc tcctgcctgg 4441agacagttga tgtgtcatgc agagctctta cttctccagc aacactcttc agtacataat 4501aagcttaact gataaacaga atatttagaa aggtgagact tgggcttacc attgggttta 4561aatcataggg acctagggcg agggttcagg gcttctctgg agcagatatt gtcaagttca 4621tggccttagg tagcatgtat ctggtcttaa ctctgattgt agcaaaagtt ctgagaggag 4681ctgagccctg ttgtggccca ttaaagaaca gggtcctcag gccctgcccg cttcctgtcc 4741actgccccct ccccatcccc agcccagccg agggaatccc gtgggttgct tacctaccta 4801taaggtggtt tataagctgc tgtcctggcc actgcattca aattccaatg tgtacttcat 4861agtgtaaaaa tttatattat tgtgaggttt tttgtctttt tttttttttt ttttttttgg 4921tatattgctg tatctacttt aacttccaga aataaacgtt atataggaac cgtaaaaa

The polypeptide sequence of human CCAAT/enhancer binding protein(C/EBP), beta (CEBPB; CEBPβ) is depicted in SEQ ID NO: 233. Thenucleotide sequence of human CEBPβ is shown in SEQ ID NO: 234. Sequenceinformation related to CEBPβ is accessible in public databases byGenBank Accession numbers NM_(—)005194 (for mRNA) and NP_(—)005185 (forprotein).

SEQ ID NO: 233 is the human wild type amino acid sequence correspondingto CEBPβ (residues 1-345), wherein the bolded sequence represents themature peptide sequence:

1 MQRLVAWDPA CLPLPPPPPA FKSMEVANFY YEADCLAAAY GGKAAPAAPP AARPGPRPPA 61GELGSIGDHE RAIDFSPYLE PLGAPQAPAP ATATDTFEAA PPAPAPAPAS SGQHHDFLSD 121LFSDDYGGKN CKKPAEYGYV SLGRLGAAKG ALHPGCFAPL HPPPPPPPPP AELKAEPGFE 181PADCKRKEEA GAPGGGAGMA AGFPYALRAY LGYQAVPSGS SGSLSTSSSS SPPGTPSPAD 241AKAPPTACYA GAAPAPSQVK SKAKKTVDKH SDEYKIRRER NNIAVRKSRD KAKMRNLETQ 301HKVLELTAEN ERLQKKVEQL SRELSTLRNL FKQLPEPLLA SSGHC

SEQ ID NO: 234 is the human wild type nucleotide sequence correspondingto CEBPβ (nucleotides 1-1837), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 gagccgcgca cgggactggg aaggggaccc acccgagggt ccagccacca gccccctcac 61taatagcggc caccccggca gcggcggcag cagcagcagc gacgcagcgg cgacagctca 121gagcagggag gccgcgccac ctgcgggccg gccggagcgg gcagccccag gccccctccc 181cgggcacccg cgttc atg ca acgcctggtg gcctgggacc cagcatgtct ccccctgccg 241ccgccgccgc ctgcctttaa atccatggaa gtggccaact tctactacga ggcggactgc 301ttggctgctg cgtacggcgg caaggcggcc cccgcggcgc cccccgcggc cagacccggg 361ccgcgccccc ccgccggcga gctgggcagc atcggcgacc acgagcgcgc catcgacttc 421agcccgtacc tggagccgct gggcgcgccg caggccccgg cgcccgccac ggccacggac 481accttcgagg cggctccgcc cgcgcccgcc cccgcgcccg cctcctccgg gcagcaccac 541gacttcctct ccgacctctt ctccgacgac tacgggggca agaactgcaa gaagccggcc 601gagtacggct acgtgagcct ggggcgcctg ggggccgcca agggcgcgct gcaccccggc 661tgcttcgcgc ccctgcaccc accgcccccg ccgccgccgc cgcccgccga gctcaaggcg 721gagccgggct tcgagcccgc ggactgcaag cggaaggagg aggccggggc gccgggcggc 781ggcgcaggca tggcggcggg cttcccgtac gcgctgcgcg cttacctcgg ctaccaggcg 841gtgccgagcg gcagcagcgg gagcctctcc acgtcctcct cgtccagccc gcccggcacg 901ccgagccccg ctgacgccaa ggcgcccccg accgcctgct acgcgggggc cgcgccggcg 961ccctcgcagg tcaagagcaa ggccaagaag accgtggaca agcacagcga cgagtacaag 1021atccggcgcg agcgcaacaa catcgccgtg cgcaagagcc gcgacaaggc caagatgcgc 1081aacctggaga cgcagcacaa ggtcctggag ctcacggccg agaacgagcg gctgcagaag 1141aaggtggagc agctgtcgcg cgagctcagc accctgcgga acttgttcaa gcagctgccc 1201gagcccctgc tcgcctcctc cggccactgc tagcgcggcc cccgcgcgcg tccccctgcc 1261ggccggggct gagactccgg ggagcgcccg cgcccgcgcc ctcgcccccg cccccggcgg 1321cgccggcaaa actttggcac tggggcactt ggcagcgcgg ggagcccgtc ggtaatttta 1381atattttatt atatatatat atctatattt ttgtccaaac caaccgcaca tgcagatggg 1441gctcccgccc gtggtgttat ttaaagaaga aacgtctatg tgtacagatg aatgataaac 1501tctctgcttc tccctctgcc cctctccagg cgccggcggg cgggccggtt tcgaagttga 1561tgcaatcggt ttaaacatgg ctgaacgcgt gtgtacacgg gactgacgca acccacgtgt 1621aactgtcagc cgggccctga gtaatcgctt aaagatgttc ctacgggctt gttgctgttg 1681atgttttgtt ttgttttgtt ttttggtctt tttttgtatt ataaaaaata atctatttct 1741atgagaaaag aggcgtctgt atattttggg aatcttttcc gtttcaagca ttaagaacac 1801ttttaataaa cttttttttg agaatggtta caaagcc

The polypeptide sequence of human CCAAT/enhancer binding protein(C/EBP), delta (CEBPD; CEBPδ) is depicted in SEQ ID NO: 235. Thenucleotide sequence of human CEBPδ is shown in SEQ ID NO: 236. Sequenceinformation related to CEBPδ is accessible in public databases byGenBank Accession numbers NM_(—)005195 (for mRNA) and NP_(—)005186 (forprotein).

SEQ ID NO: 235 is the human wild type amino acid sequence correspondingto CEBPδ (residues 1-269), wherein the bolded sequence represents themature peptide sequence:

1 MSAALFSLDG PARGAPWPAE PAPFYEPGRA GKPGRGAEPG ALGEPGAAAP AMYDDESAID 61FSAYIDSMAA VPTLELCHDE LFADLFNSNH KAGGAGPLEL LPGGPARPLG PGPAAPRLLK 121REPDWGDGDA PGSLLPAQVA ACAQTVVSLA AAGQPTPPTS PEPPRSSPRQ TPAPGPAREK 181SAGKRGPDRG SPEYRQRRER NNIAVRKSRD KAKRRNQEMQ QKLVELSAEN EKLHQRVEQL 241TRDLAGLRQF FKQLPSPPFL PAAGTADCR

SEQ ID NO: 236 is the human wild type nucleotide sequence correspondingto CEBPδ (nucleotides 1-1269), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 aggtgacagc ctcgcttgga cgcagagccc ggcccgacgc cgcc atg agc gccgcgctct 61tcagcctgga cggcccggcg cgcggcgcgc cctggcctgc ggagcctgcg cccttctacg 121aaccgggccg ggcgggcaag ccgggccgcg gggccgagcc aggggcccta ggcgagccag 181gcgccgccgc ccccgccatg tacgacgacg agagcgccat cgacttcagc gcctacatcg 241actccatggc cgccgtgccc accctggagc tgtgccacga cgagctcttc gccgacctct 301tcaacagcaa tcacaaggcg ggcggcgcgg ggcccctgga gcttcttccc ggcggccccg 361cgcgcccctt gggcccgggc cctgccgctc cccgcctgct caagcgcgag cccgactggg 421gcgacggcga cgcgcccggc tcgctgttgc ccgcgcaggt ggccgcgtgc gcacagaccg 481tggtgagctt ggcggccgca gggcagccca ccccgcccac gtcgccggag ccgccgcgca 541gcagccccag gcagaccccc gcgcccggcc ccgcccggga gaagagcgcc ggcaagaggg 601gcccggaccg cggcagcccc gagtaccggc agcggcgcga gcgcaacaac atcgccgtgc 661gcaagagccg cgacaaggcc aagcggcgca accaggagat gcagcagaag ttggtggagc 721tgtcggctga gaacgagaag ctgcaccagc gcgtggagca gctcacgcgg gacctggccg 781gcctccggca gttcttcaag cagctgccca gcccgccctt cctgccggcc gccgggacag 841cagactgccg gtaacgcgcg gccggggcgg gagagactca gcaacgaccc atacctcaga 901cccgacggcc cggagcggag cgcgccctgc cctggcgcag ccagagccgc cgggtgcccg 961ctgcagtttc ttgggacata ggagcgcaaa gaagctacag cctggactta ccaccactaa 1021actgcgagag aagctaaacg tgtttatttt cccttaaatt atttttgtaa tggtagcttt 1081ttctacatct tactcctgtt gatgcagcta aggtacattt gtaaaaagaa aaaaaaccag 1141acttttcaga caaacccttt gtattgtaga taagaggaaa agactgagca tgctcacttt 1201tttatattaa tttttacagt atttgtaaga ataaagcagc atttgaaatc gaaaaaaaaa 1261aaaaaaaaa

The polypeptide sequence of human runt-related transcription factor 1isoform AML1b (RunX1) is depicted in SEQ ID NO: 237. The nucleotidesequence of human RunX1 is shown in SEQ ID NO: 238. Sequence informationrelated to RunX1 is accessible in public databases by GenBank Accessionnumbers NM_(—)001001890 (for mRNA) and NP_(—)001001890 (for protein).

SEQ ID NO: 237 is the human wild type amino acid sequence correspondingto RunX1 (residues 1-453), wherein the bolded sequence represents themature peptide sequence:

1 MRIPVDASTS RRFTPPSTAL SPGKMSEALP LGAPDAGAAL AGKLRSGDRS MVEVLADHPG 61ELVRTDSPNF LCSVLPTHWR CNKTLPIAFK VVALGDVPDG TLVTVMAGND ENYSAELRNA 121TAAMKNQVAR FNDLRFVGRS GRGKSFTLTI TVFTNPPQVA TYHRAIKITV DGPREPRRHR 181QKLDDQTKPG SLSFSERLSE LEQLRRTAMR VSPHHPAPTP NPRASLNHST AFNPQPQSQM 241QDTRQIQPSP PWSYDQSYQY LGSIASPSVH PATPISPGRA SGMTTLSAEL SSRLSTAPDL 301TAFSDPRQFP ALPSISDPRM HYPGAFTYSP TPVTSGIGIG MSAMGSATRY HTYLPPPYPG 361SSQAQGGPFQ ASSPSYHLYY GASAGSYQFS MVGGERSPPR ILPPCTNAST GSALLNPSLP 421NQSDVVEAEG SHSNSPTNMA PSARLEEAVW RPY

SEQ ID NO: 238 is the human wild type nucleotide sequence correspondingto RunX1 (nucleotides 1-7274), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 catagagcca gcgggcgcgg gcgggacggg cgccccgcgg ccggacccag ccagggcacc 61acgctgcccg gccctgcgcc gccaggcact tctttccggg gctcctaggg acgccagaag 121gaagtcaacc tctgctgctt ctccttggcc tgcgttggac cttccttttt ttgttgtttt 181tttttgtttt tcccctttct tccttttgaa ttaactggct tcttggctgg atgttttcaa 241cttctttcct ggctgcgaac ttttccccaa ttgttttcct tttacaacag ggggagaaag 301tgctctgtgg tccgaggcga gccgtgaagt tgcgtgtgcg tggcagtgtg cgtggcagga 361tgtgcgtgcg tgtgtaaccc gagccgcccg atctgtttcg atctgcgccg cggagccctc 421cctcaaggcc cgctccacct gctgcggtta cgcggcgctc gtgggtgttc gtgcctcgga 481gcagctaacc ggcgggtgct gggcgacggt ggaggagtat cgtctcgctg ctgcccgagt 541cagggctgag tcacccagct gatgtagaca gtggctgcct tccgaagagt gcgtgtttgc 601atgtgtgtga ctctgcggct gctcaactcc caacaaacca gaggaccagc cacaaactta 661accaacatcc ccaaacccga gttcacagat gtgggagagc tgtagaaccc tgagtgtcat 721cgactgggcc ttcttatgat tgttgtttta agattagctg aagatctctg aaacgctgaa 781ttttctgcac tgagcgtttt gacagaattc attgagagaa cagagaacat gacaagtact 841tctagctcag cactgctcca actactgaag ctgattttca aggctactta aaaaaatctg 901cagcgtacat taatggattt ctgttgtgtt taaattctcc acagattgta ttgtaaatat 961tttatgaagt agagcatatg tatatattta tatatacgtg cacatacatt agtagcacta 1021cctttggaag tctcagctct tgcttttcgg gactgaagcc agttttgcat gataaaagtg 1081gccttgttac gggagataat tgtgttctgt tgggacttta gacaaaactc acctgcaaaa 1141aactgacagg cattaactac tggaacttcc aaataatgtg tttgctgatc gttttactct 1201tcgcataaat attttaggaa gtgtatgaga attttgcctt caggaacttt tctaacagcc 1261aaagacagaa cttaacctct gcaagcaaga ttcgtggaag atagtctcca ctttttaatg 1321cactaagcaa tcggttgcta ggagcccatc ctgggtcaga ggccgatccg cagaaccaga 1381acgttttccc ctcctggact gttagtaact tagtctccct cctcccctaa ccacccccgc 1441ccccccccac cccccgcagt aataaaggcc cctgaacgtg tatgttggtc tcccgggagc 1501tgcttgctga agatccgcgc ccctgtcgcc gtctggtagg agctgtttgc agggtcctaa 1561ctcaatcggc ttgttgtg at g cgtatcccc gtagatgcca gcacgagccg ccgcttcacg 1621ccgccttcca ccgcgctgag cccaggcaag atgagcgagg cgttgccgct gggcgccccg 1681gacgccggcg ctgccctggc cggcaagctg aggagcggcg accgcagcat ggtggaggtg 1741ctggccgacc acccgggcga gctggtgcgc accgacagcc ccaacttcct ctgctccgtg 1801ctgcctacgc actggcgctg caacaagacc ctgcccatcg ctttcaaggt ggtggcccta 1861ggggatgttc cagatggcac tctggtcact gtgatggctg gcaatgatga aaactactcg 1921gctgagctga gaaatgctac cgcagccatg aagaaccagg ttgcaagatt taatgacctc 1981aggtttgtcg gtcgaagtgg aagagggaaa agcttcactc tgaccatcac tgtcttcaca 2041aacccaccgc aagtcgccac ctaccacaga gccatcaaaa tcacagtgga tgggccccga 2101gaacctcgaa gacatcggca gaaactagat gatcagacca agcccgggag cttgtccttt 2161tccgagcggc tcagtgaact ggagcagctg cggcgcacag ccatgagggt cagcccacac 2221cacccagccc ccacgcccaa ccctcgtgcc tccctgaacc actccactgc ctttaaccct 2281cagcctcaga gtcagatgca ggatacaagg cagatccaac catccccacc gtggtcctac 2341gatcagtcct accaatacct gggatccatt gcctctcctt ctgtgcaccc agcaacgccc 2401atttcacctg gacgtgccag cggcatgaca accctctctg cagaactttc cagtcgactc 2461tcaacggcac ccgacctgac agcgttcagc gacccgcgcc agttccccgc gctgccctcc 2521atctccgacc cccgcatgca ctatccaggc gccttcacct actccccgac gccggtcacc 2581tcgggcatcg gcatcggcat gtcggccatg ggctcggcca cgcgctacca cacctacctg 2641ccgccgccct accccggctc gtcgcaagcg cagggaggcc cgttccaagc cagctcgccc 2701tcctaccacc tgtactacgg cgcctcggcc ggctcctacc agttctccat ggtgggcggc 2761gagcgctcgc cgccgcgcat cctgccgccc tgcaccaacg cctccaccgg ctccgcgctg 2821ctcaacccca gcctcccgaa ccagagcgac gtggtggagg ccgagggcag ccacagcaac 2881tcccccacca acatggcgcc ctccgcgcgc ctggaggagg ccgtgtggag gccctactga 2941ggcgccaggc ctggcccggc tgggccccgc gggccgccgc cttcgcctcc gggcgcgcgg 3001gcctcctgtt cgcgacaagc ccgccgggat cccgggccct gggcccggcc accgtcctgg 3061ggccgagggc gcccgacggc caggatctcg ctgtaggtca ggcccgcgca gcctcctgcg 3121cccagaagcc cacgccgccg ccgtctgctg gcgccccggc cctcgcggag gtgtccgagg 3181cgacgcacct cgagggtgtc cgccggcccc agcacccagg ggacgcgctg gaaagcaaac 3241aggaagattc ccggagggaa actgtgaatg cttctgattt agcaatgctg tgaataaaaa 3301gaaagatttt atacccttga cttaactttt taaccaagtt gtttattcca aagagtgtgg 3361aattttggtt ggggtggggg gagaggaggg atgcaactcg ccctgtttgg catctaattc 3421ttatttttaa tttttccgca ccttatcaat tgcaaaatgc gtatttgcat ttgggtggtt 3481tttattttta tatacgttta tataaatata tataaattga gcttgcttct ttcttgcttt 3541gaccatggaa agaaatatga ttcccttttc tttaagtttt atttaacttt tcttttggac 3601ttttgggtag ttgttttttt ttgttttgtt ttgttttttt gagaaacagc tacagctttg 3661ggtcattttt aactactgta ttcccacaag gaatccccag atatttatgt atcttgatgt 3721tcagacattt atgtgttgat aattttttaa ttatttaaat gtacttatat taagaaaaat 3781atcaagtact acattttctt ttgttcttga tagtagccaa agttaaatgt atcacattga 3841agaaggctag aaaaaaagaa tgagtaatgt gatcgcttgg ttatccagaa gtattgttta 3901cattaaactc cctttcatgt taatcaaaca agtgagtagc tcacgcagca acgtttttaa 3961taggattttt agacactgag ggtcactcca aggatcagaa gtatggaatt ttctgccagg 4021ctcaacaagg gtctcatatc taacttcctc cttaaaacag agaaggtcaa tctagttcca 4081gagggttgag gcaggtgcca ataattacat ctttggagag gatttgattt ctgcccaggg 4141atttgctcac cccaaggtca tctgataatt tcacagatgc tgtgtaacag aacacagcca 4201aagtaaactg tgtaggggag ccacatttac ataggaacca aatcaatgaa tttaggggtt 4261acgattatag caatttaagg gcccaccaga agcaggcctc gaggagtcaa tttgcctctg 4321tgtgcctcag tggagacaag tgggaaaaca tggtcccacc tgtgcgagac cccctgtcct 4381gtgctgctca ctcaacaaca tctttgtgtt gctttcacca ggctgagacc ctaccctatg 4441gggtatatgg gcttttacct gtgcaccagt gtgacaggaa agattcatgt cactactgtc 4501cgtggctaca attcaaaggt atccaatgtc gctgtaaatt ttatggcact atttttattg 4561gaggatttgg tcagaatgca gttgttgtac aactcataaa tactaactgc tgattttgac 4621acatgtgtgc tccaaatgat ctggtggtta tttaacgtac ctcttaaaat tcgttgaaac 4681gatttcaggt caactctgaa gagtatttga aagcaggact tcagaacagt gtttgatttt 4741tattttataa atttaagcat tcaaattagg caaatctttg gctgcaggca gcaaaaacag 4801ctggacttat ttaaaacaac ttgtttttga gttttcttat atatatattg attatttgtt 4861ttacacacat gcagtagcac tttggtaaga gttaaagagt aaagcagctt atgttgtcag 4921gtcgttctta tctagagaag agctatagca gatctcggac aaactcagaa tatattcact 4981ttcatttttg acaggattcc ctccacaact cagtttcata tattattccg tattacattt 5041ttgcagctaa attaccataa aatgtcagca aatgtaaaaa tttaatttct gaaaagcacc 5101attagcccat ttcccccaaa ttaaacgtaa atgttttttt tcagcacatg ttaccatgtc 5161tgacctgcaa aaatgctgga gaaaaatgaa ggaaaaaatt atgtttttca gtttaattct 5221gttaactgaa gatattccaa ctcaaaacca gcctcatgct ctgattagat aatcttttac 5281attgaacctt tactctcaaa gccatgtgtg gagggggctt gtcactattg taggctcact 5341ggattggtca tttagagttt cacagactct taccagcata tatagtattt aattgtttca 5401aaaaaaatca aactgtagtt gttttggcga taggtctcac gcaacacatt tttgtatgtg 5461tgtgtgtgtg cgtgtgtgtg tgtgtgtgtg aaaaattgca ttcattgact tcaggtagat 5521taaggtatct ttttattcat tgccctcagg aaagttaagg tatcaatgag acccttaagc 5581caatcatgta ataactgcat gtgtctggtc caggagaagt attgaataag ccatttctac 5641tgcttactca tgtccctatt tatgatttca acatggatac atatttcagt tctttctttt 5701tctcactatc tgaaaataca tttccctccc tctcttcccc ccaatatctc cctttttttc 5761tctcttcctc tatcttccaa accccacttt ctccctcctc cttttcctgt gttctcttaa 5821gcagatagca cataccccca cccagtacca aatttcagaa cacaagaagg tccagttctt 5881cccccttcac ataaaggaac atggtttgtc agcctttctc ctgtttatgg gtttcttcca 5941gcagaacaga gacattgcca accatattgg atctgcttgc tgtccaaacc agcaaacttt 6001cctgggcaaa tcacaatcag tgagtaaata gacagccttt ctgctgcctt gggtttctgt 6061gcagataaac agaaatgctc tgattagaaa ggaaatgaat ggttccactc aaatgtcctg 6121caatttagga ttgcagattt ctgccttgaa atacctgttt ctttgggaca ttccgtcctg 6181atgattttta tttttgttgg tttttatttt tggggggaat gacatgtttg ggtcttttat 6241acatgaaaat ttgtttgaca ataatctcac aaaacatatt ttacatctga acaaaatgcc 6301tttttgttta ccgtagcgta tacatttgtt ttgggatttt tgtgtgtttg ttgggaattt 6361tgtttttagc caggtcagta ttgatgaggc tgatcatttg gctctttttt tccttccaga 6421agagttgcat caacaaagtt aattgtattt atgtatgtaa atagatttta agcttcatta 6481taaaatattg ttaatgccta taactttttt tcaatttttt tgtgtgtgtt tctaaggact 6541ttttcttagg tttgctaaat actgtaggga aaaaaatgct tctttctact ttgtttattt 6601tagactttaa aatgagctac ttcttattca cttttgtaaa cagctaatag catggttcca 6661atttttttta agttcacttt ttttgttcta ggggaaatga atgtgcaaaa aaagaaaaag 6721aactgttggt tatttgtgtt attctggatg tataaaaatc aatggaaaaa aataaacttt 6781caaattgaaa tgacggtata acacatctac tgaaaaagca acgggaaatg tggtcctatt 6841taagccagcc cccacctagg gtctatttgt gtggcagtta ttgggtttgg tcacaaaaca 6901tcctgaaaat tcgtgcgtgg gcttctttct ccctggtaca aacgtatgga atgcttctta 6961aaggggaact gtcaagctgg tgtcttcagc cagatgacat gagagaatat cccagaaccc 7021tctctccaag gtgtttctag atagcacagg agagcaggca ctgcactgtc cacagtccac 7081ggtacacagt cgggtgggcc gcctcccctc tcctgggagc attcgtcgtg cccagcctga 7141gcagggcagc tggactgctg ctgttcagga gccaccagag ccttcctctc tttgtaccac 7201agtttcttct gtaaatccag tgttacaatc agtgtgaatggcaaataaaca gtttgacaa 7261gtacatacac cata

The polypeptide sequence of human FOS-like antigen 2 (FOSL2) is depictedin SEQ ID NO: 239. The nucleotide sequence of human FOSL2 is shown inSEQ ID NO: 240. Sequence information related to FOSL2 is accessible inpublic databases by GenBank Accession numbers NM_(—)005253 (for mRNA)and NP_(—)005244 (for protein).

SEQ ID NO: 239 is the human wild type amino acid sequence correspondingto FOSL2 (residues 1-326), wherein the bolded sequence represents themature peptide sequence:

1 MYQDYPGNFD TSSRGSSGSP AHAESYSSGG GGQQKFRVDM PGSGSAFIPT INAITTSQDL 61QWMVQPTVIT SMSNPYPRSH PYSPLPGLAS VPGHMALPRP GVIKTIGTTV GRRRRDEQLS 121PEEEEKRRIR RERNKLAAAK CRNRRRELTE KLQAETEELE EEKSGLQKEI AELQKEKEKL 181EFMLVAHGPV CKISPEERRS PPAPGLQPMR SGGGSVGAVV VKQEPLEEDS PSSSSAGLDK 241AQRSVIKPIS IAGGFYGEEP LHTPIVVTST PAVTPGTSNL VFTYPSVLEQ ESPASPSESC 301SKAHRRSSSS GDQSSDSLNS PTLLAL

SEQ ID NO: 240 is the human wild type nucleotide sequence correspondingto FOSL2 (nucleotides 1-4015), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 cgaacgagcg gcgctcggcg gggacagaaa gagggagaga gagagagaga gagagggaga 61ggcgcggccg ggcgaggcgg gcccgtccgg gagcgggctc cggggaaggg gtgcgggtct 121gggcgccgga gcggggagcg gggccgcgtc cctctcagcg ccagctctac ttgagcccca 181cgagccgctg tccccctggc gcgctcgggg ccgcgggacg ggcgcacgcc gccttctcct 241agtcaagtat ccgagccgcc ccgaaactcg ggcggcgagt cggccacggg aagtttattc 301tccggctcct tttctaaaag gaagaaacag aagtttctcc cagcggacag cttttctttc 361cgcctttttg gccctgtctg aaatcggggg tccccagggc tggcaggcca ggctcgctgg 421gctcctaatc ttttttttaa tttccaattt ttgattgggc cgtgggtccc cgctgagctc 481cggctgcgcg cgggggcggg agggcgcgcg caggggaggg accgagagac gcgccgactt 541tttagaggga gggatcgggt ggacaactgg tcccgcggcg ctcgcagagc cggaaagaag 601tgctgtaagg gacgctcggg ggacgctgtt cctgaggtgt cgccgcctcc ctgtcctcgc 661cctccgcggt gggggagaaa cccaggagcg aagcccagag cccgcggcgc ggccggcgga 721cgaacgagcg cgcagcagcc ggtgcgcggc cgcggcgagg gcgggggaag aaaaacaccc 781tgtttcctct ccggccccca ccgcggatc a tg taccagga ttatcccggg aactttgaca 841cctcgtcccg gggcagcagc ggctctcctg cgcacgccga gtcctactcc agcggcggcg 901gcggccagca gaaattccgg gtagatatgc ctggctcagg cagtgcattc atccccacca 961tcaacgccat cacgaccagc caggacctgc agtggatggt gcagcccaca gtgatcacct 1021ccatgtccaa cccataccct cgctcgcacc cctacagccc cctgccgggc ctggcctctg 1081tccctggaca catggccctc ccaagacctg gcgtgatcaa gaccattggc accaccgtgg 1141gccgcaggag gagagatgag cagctgtctc ctgaagagga ggagaagcgt cgcatccggc 1201gggagaggaa caagctggct gcagccaagt gccggaaccg acgccgggag ctgacagaga 1261agctgcaggc ggagacagag gagctggagg aggagaagtc aggcctgcag aaggagattg 1321ctgagctgca gaaggagaag gagaagctgg agttcatgtt ggtggctcac ggcccagtgt 1381gcaagattag ccccgaggag cgccgatcgc ccccagcccc tgggctgcag cccatgcgca 1441gtgggggtgg ctcggtgggc gctgtagtgg tgaaacagga gcccctggaa gaggacagcc 1501cctcgtcctc gtcggcgggg ctggacaagg cccagcgctc tgtcatcaag cccatcagca 1561ttgctggggg cttctacggt gaggagcccc tgcacacccc catcgtggtg acctccacac 1621ctgctgtcac tccgggcacc tcgaacctcg tcttcaccta tcctagcgtc ctggagcagg 1681agtcacccgc atctccctcc gaatcctgct ccaaggctca ccgcagaagc agtagcagcg 1741gggaccaatc atcagactcc ttgaactccc ccactctgct ggctctgtaa cccagtgcac 1801ctccctcccc agctccggag ggggtcctcc tcgctcctcc ttcccaggga ccagcacctt 1861caagcgctcc agggccgtga gggcaagagg gggacctgcc accagggagc ttcctggctc 1921tgggggaccc aggtgggact tagcagtgag tattggaaga cttgggttga tctcttagaa 1981gccatgggac ctcctccctc attcatcttg caagcaaatc ccatttcttg aaaagccttg 2041gagaactcgg tttggtagac ttggacatct ctctggcttc tgaagagcct gaagctggcc 2101tggaccattc ctgtcccttt gttaccatac tgtctctgga gtgatggtgt ccttccctgc 2161cccaccacgc atgctcagtg ccttttggtt tcaccttccc tcgacttgac cctttcctcc 2221cccagcgtca gtttcactcc ctcttggttt ttatcaaatt tgccatgaca tttcatctgg 2281gtggtctgaa tattaaagct cttcatttct ggagatgggg cagcaggtgg ctcttctgct 2341ggggctgact tgtccagaag gggacaaagt gcaatacaga gccttcccta ccctgacgcc 2401tcccagtcat catctccaga actcccagcg gggctccctg agctctcaag gagatgctgc 2461catcactggg aggctcagag gacccttcct gcccaccttc ggagacggct tctggaggaa 2521cggcttggcc agaagacagg gtgtgagtga gacagtgggg cacaggttgg gtttgccaaa 2581cgcctaatta ccaggccagg aagcatgcca acaaagccac acgggtgtcc tagccagctt 2641cccttcacct ggtgtcttga gtagggcgtc tcctgtaatt actgccttgc cattctgccc 2701ctggaccctt ctctccggac cagggaggcg tccctcccta ggagccacac attatactcc 2761aagtccctgc cgggctccgc ctttccccca ccctggctct cagggtgacg ccaccacag 2821agatttaatg agcgtgggcc tggaccttcc ccagatgctg ccaggcagcc cctccccaag 2881cctcaaagaa gcatttgctg aggatggaga ggcaggggag ggaggcggga ggccgtcact 2941ggagtggcgt ctgcagcagc tgctgcccca gcacccgctc agcctgtcct ggctgctcac 3001ctccccgcag ggcaccgggc ctttcctgcc ctctgtggtc atctgccacc tgctggatca 3061agtgctttct cttttacact cccctgtccc caccccagtg cactcttctg gcccaggcag 3121caagcaagct gtgaacagct ggcctgagct gtcgctgtgg cttgtggctc atgcgccatt 3181cctggttgtc tgttgaatct ttctggctgc tggaattgga gataggatgt tttgcttccc 3241actgcaggag agctgccccc tttcacgggg ttggggaagg gtccccctgg cctccagcag 3301gagcacagct cagcagggtc cctgctgccc acccctctga gccttttctc cccagggtat 3361ggctcctgct gagtttcttg tccagcaggg ccttgacagg aatccaggga gtagctcctg 3421gccagaacca gcctctgcgg ggcttgtgct ctgcaaagac tctgctgctg gggattcagc 3481tctagaggtc acagtatcct cgtttgaaag ataattaaga tcccccgtgg agaaagcagt 3541gacacattca cacagctgtt ccctcgcatg ttatttcatg aacatgacct gttttcgtgc 3601actagacaca cagagtggaa cagccgtatg cttaaagtac atgggccagt gggactggaa 3661gtgacctgta caagtgatgc agaaaggagg gtttcaaaga aaaaggattt tgtttaaaat 3721actttaaaaa tgttatttcc tgcatccctt ggctgtgatg cccctctccc gatttcccag 3781gggctctggg agggaccctt ctaagaagat tgggcagttg ggtttctggc ttgagatgaa 3841tccaagcagc agaatgagcc aggagtagca ggagatgggc aaagaaaact ggggtgcact 3901cagctctcac aggggtaatc atctcaagtg gtatttgtag ccaagtggga gctattttct 3961tttttgtgca tatagatatt tcttaaatga aaaaaaaaaa aaaaaaaaaa aaaaa

Class E basic helix-loop-helix protein 40 is a protein that in humans isencoded by the BHLHE40 gene, also referred to as BHLHB2 (bHLH-B2, asused herein). BHLHB2 is depicted in SEQ ID NO: 241. The nucleotidesequence of human BHLHB2 is shown in SEQ ID NO: 242. Sequenceinformation related to BHLHB2 is accessible in public databases byGenBank Accession numbers NM_(—)003670 (for mRNA) and NP_(—)003661 (forprotein).

SEQ ID NO: 241 is the human wild type amino acid sequence correspondingto BHLHB2 (residues 1-412), wherein the bolded sequence represents themature peptide sequence:

1 MERIPSAQPP PACLPKAPGL EHGDLPGMYP AHMYQVYKSR RGIKRSEDSK ETYKLPHRLI 61EKKRRDRINE CIAQLKDLLP EHLKLTTLGH LEKAVVLELT LKHVKALTNL IDQQQQKIIA 121LQSGLQAGEL SGRNVETGQE MFCSGFQTCA REVLQYLAKH ENTRDLKSSQ LVTHLHRVVS 181ELLQGGTSRK PSDPAPKVMD FKEKPSSPAK GSEGPGKNCV PVIQRTFAHS SGEQSGSDTD 241TDSGYGGESE KGDLRSEQPC FKSDHGRRFT MGERIGAIKQ ESEEPPTKKN RMQLSDDEGH 301FTSSDLISSP FLGPHPHQPP FCLPFYLIPP SATAYLPMLE KCWYPTSVPV LYPGLNASAA 361ALSSFMNPDK ISAPLLMPQR LPSPLPAHPS VDSSVLLQAL KPIPPLNLET KD

SEQ ID NO: 242 is the human wild type nucleotide sequence correspondingto BHLHB2 (nucleotides 1-3061), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 cgcctccccg cccgccccac ttctcattca cttggctcgc acggcgcaga cagaccgcgc 61agggagcaca caccgccagt ctgtgcgctg agtcggagcc agaggccgcg gggacaccgg 121gccatgcacg cccccaactg aagctgcatc tcaaagccga agattccagc agcccagggg 181atttcaaaga gctcagactc agaggaacat ctgcggagag acccccgaag ccctctccag 241ggcagtcctc atccagacgc tccgctagtg cagacaggag cgcgcagtgg ccccggctcg 301ccgcgcc atg  gagcggatcc ccagcgcgca accacccccc gcctgcctgc ccaaagcacc 361gggactggag cacggagacc taccagggat gtaccctgcc cacatgtacc aagtgtacaa 421gtcaagacgg ggaataaagc ggagcgagga cagcaaggag acctacaaat tgccgcaccg 481gctcatcgag aaaaagagac gtgaccggat taacgagtgc atcgcccagc tgaaggatct 541cctacccgaa catctcaaac ttacaacttt gggtcacttg gaaaaagcag tggttcttga 601acttaccttg aagcatgtga aagcactaac aaacctaatt gatcagcagc agcagaaaat 661cattgccctg cagagtggtt tacaagctgg tgagctgtca gggagaaatg tcgaaacagg 721tcaagagatg ttctgctcag gtttccagac atgtgcccgg gaggtgcttc agtatctggc 781caagcacgag aacactcggg acctgaagtc ttcgcagctt gtcacccacc tccaccgggt 841ggtctcggag ctgctgcagg gtggtacctc caggaagcca tcagacccag ctcccaaagt 901gatggacttc aaggaaaaac ccagctctcc ggccaaaggt tcggaaggtc ctgggaaaaa 961ctgcgtgcca gtcatccagc ggactttcgc tcactcgagt ggggagcaga gcggcagcga 1021cacggacaca gacagtggct atggaggaga atcggagaag ggcgacttgc gcagtgagca 1081gccgtgcttc aaaagtgacc acggacgcag gttcacgatg ggagaaagga tcggcgcaat 1141taagcaagag tccgaagaac cccccacaaa aaagaaccgg atgcagcttt cggatgatga 1201aggccatttc actagcagtg acctgatcag ctccccgttc ctgggcccac acccacacca 1261gcctcctttc tgcctgccct tctacctgat cccaccttca gcgactgcct acctgcccat 1321gctggagaag tgctggtatc ccacctcagt gccagtgcta tacccaggcc tcaacgcctc 1381tgccgcagcc ctctctagct tcatgaaccc agacaagatc tcggctccct tgctcatgcc 1441ccagagactc ccttctccct tgccagctca tccgtccgtc gactcttctg tcttgctcca 1501agctctgaag ccaatccccc ctttaaactt agaaaccaaa gactaaactc tctaggggat 1561cctgctgctt tgctttcctt cctcgctact tcctaaaaag caacaaaaaa gtttttgtga 1621atgctgcaag attgttgcat tgtgtatact gagataatct gaggcatgga gagcagattc 1681agggtgtgtg tgtgtgtgtg tgtgtgtgta tgtgcgtgtg cgtgcacatg tgtgcctgcg 1741tgttggtata ggactttaaa gctccttttg gcatagggaa gtcacgaagg attgcttgac 1801atcaggagac ttggggggga ttgtagcaga cgtctgggct tttccccacc cagagaatag 1861cccccttcga tacacatcag ctggattttc aaaagcttca aagtcttggt ctgtgagtca 1921ctcttcagtt tgggagctgg gtctgtggct ttgatcagaa ggtactttca aaagagggct 1981ttccagggct cagctcccaa ccagctgtta ggaccccacc cttttgcctt tattgtcgac 2041gtgactcacc agacgtcggg gagagagagc agtcagaccg agctttctgc taacatgggg 2101aggtagcagg cactggcata gcacggtagt ggtttgggga ggtttccgca ggtctgctcc 2161ccacccctgc ctcggaagaa taaagagaat gtagttccct actcaggctt tcgtagtgat 2221tagcttacta aggaactgaa aatgggcccc ttgtacaagc tgagctgccc cggagggagg 2281gaggagttcc ctgggcttct ggcacctgtt tctaggccta accattagta cttactgtgc 2341agggaaccaa accaaggtct gagaaatgcg gacaccccga gcgagcaccc caaagtgcac 2401aaagctgagt aaaaagctgc ccccttcaaa cagaactaga ctcagttttc aattccatcc 2461taaaactcct tttaaccaag cttagcttct caaaggccta accaagcctt ggcaccgcca 2521gatcctttct gtaggctaat tcctcttgcc caacggcata tggagtgtcc ttattgctaa 2581aaaggattcc gtctccttca aagaagtttt atttttggtc cagagtactt gttttcccga 2641tgtgtccagc cagctccgca gcagcttttc aaaatgcact atgcctgatt gctgatcgtg 2701ttttaacttt ttcttttcct gtttttattt tggtattaag tcgttgcctt tatttgtaaa 2761gctgttataa atatatatta tataaatata ttaaaaagga aaatgtttca gatgtttatt 2821tgtataatta cttgattcac acagtgagaa aaaatgaatg tattcctgtt tttgaagaga 2881agaataattt tttttttctc tagggagagg tacagtgttt atattttgga gccttcctga 2941aggtgtaaaa ttgtaaatat ttttatctat gagtaaatgt taagtagttg ttttaaaata 3001cttaataaaa taattctttt cctgtggaag agaaaaaaaa aaaaaaaaaa aaaaaaaaaa 3061 a

The polypeptide sequence of human zinc finger protein 238 isoform 2(ZNF238) is depicted in SEQ ID NO: 243. The nucleotide sequence of humanZNF238 is shown in SEQ ID NO: 244. Sequence information related toZNF238 is accessible in public databases by GenBank Accession numbersNM_(—)006352 (for mRNA) and NP_(—)006343 (for protein).

SEQ ID NO: 243 is the human wild type amino acid sequence correspondingto ZNF238 (residues 1-522), wherein the bolded sequence represents themature peptide sequence:

1 MEFPDHSRHL LQCLSEQRHQ GFLCDCTVLV GDAQFRAHRA VLASCSMYFH LFYKDQLDKR 61DIVHLNSDIV TAPAFALLLE FMYEGKLQFK DLPIEDVLAA ASYLHMYDIV KVCKKKLKEK 121ATTEADSTKK EEDASSCSDK VESLSDGSSH IAGDLPSDED EGEDEKLNIL PSKRDLAAEP 181GNMWMRLPSD SAGIPQAGGE AEPHATAAGK TVASPCSSTE SLSQRSVTSV RDSADVDCVL 241DLSVKSSLSG VENLNSSYFS SQDVLRSNLV QVKVEKEASC DESDVGTNDY DMEHSTVKES 301VSTNNRVQYE PAHLAPLRED SVLRELDRED KASDDEMMTP ESERVQVEGG MESSLLPYVS 361NILSPAGQIF MCPLCNKVFP SPHILQIHLS THFREQDGIR SKPAADVNVP TCSLCGKTFS 421CMYTLKRHER THSGEKPYTC TQCGKSFQYS HNLSRHAVVH TREKPHACKW CERRFTQSGD 481LYRHIRKFHC ELVNSLSVKS EALSLPTVRD WTLEDSSQEL WK

SEQ ID NO: 244 is the human wild type nucleotide sequence correspondingto ZNF238 (nucleotides 1-4244), wherein the underscored bolded “ATG”denotes the beginning of the open reading frame:

1 tttaaactgt gctttctaag cacagtcagg tagcaaaagt aataaaaagg atggttgaac 61aagttttctt gtatgttcca ggatatgttt gggacttttc tttgtttatt atatgagttg 121ttccctttga aattaaagct attttgtagg ttttgtggga cataatttga taagtagagt 181taattaaatt tcttctggaa gagatctaaa ttcttattct tagtgagaga ctgtagttaa 241aggaaggctt ttagaacttg ggttcaagga agatggagat gcgtcggaag ctctttggcg 301ggggtgagga agttcagaaa gtgtgcattt tccttctggc atttaggtct tgtccgtgtg 361atttggtggt gcttgggtca taagcctgat taaaattcag ggacatgtac cacggcggcc 421aaagcggaat taattttttt atatggggac tggagcgctg aaaagttgtt cctgaccagg 481ctctaatgag aaattcctct ctccccaggt tatgaagaca gt atg gagtt tccagaccat 541agtagacatt tgctacagtg tctgagcgag cagagacacc agggttttct ttgtgactgc 601actgttctgg tgggagatgc ccagttccga gcgcaccgag ctgtactggc ttcatgcagc 661atgtatttcc acctctttta caaggaccag ctggacaaaa gagacattgt tcatctgaac 721agcgacattg ttacagcccc cgctttcgct ctcctgcttg aattcatgta tgaagggaaa 781ctccagttca aagacttgcc cattgaagac gtgctagcag ctgccagtta tctccacatg 841tatgacattg tcaaagtctg caaaaagaag ctgaaagaga aagccaccac ggaggcagac 901agcaccaaaa aggaagaaga tgcttcaagt tgttcggaca aagtcgagag tctctccgat 961ggcagcagcc acatagcagg cgatttgccc agtgatgaag atgaaggaga agatgaaaaa 1021ttgaacatcc tgcccagcaa aagggacttg gcggccgagc ctgggaacat gtggatgcga 1081ttgccctcag actcagcagg catcccccag gctggcggag aggcagagcc acacgccaca 1141gcagctggaa aaacagtagc cagcccctgc agctcaacag agtctttgtc ccagaggtct 1201gtcacctccg tgagggattc ggcagatgtt gactgtgtgc tggacctgtc tgtcaagtcc 1261agcctttcag gagttgaaaa tctgaacagc tcttatttct cttcacagga cgtgctgaga 1321agcaacctgg tgcaggtgaa ggtggagaaa gaggcttcct gtgatgagag tgatgttggc 1381actaatgact atgacatgga acatagcact gtgaaagaaa gtgtgagcac taataacagg 1441gtacagtatg agccggccca tctggctccc ctgagggagg actcggtctt gagggagctg 1501gaccgggagg acaaagccag tgatgatgag atgatgaccc cagagagcga gcgtgtccag 1561gtggagggag gcatggagag cagtctgctc ccctacgtct ccaacatcct gagccccgcg 1621ggccagatct tcatgtgccc cctgtgcaac aaggtcttcc ccagccccca catcctgcag 1681atccacctga gcacgcactt ccgcgagcag gacggcatcc gcagcaagcc cgccgccgat 1741gtcaacgtgc ccacgtgctc gctgtgtggg aagactttct cttgcatgta caccctcaag 1801cgccacgaga ggactcactc gggggagaag ccctacacat gcacccagtg cggcaagagc 1861ttccagtact cgcacaacct gagccgccat gccgtggtgc acacccgcga gaagccgcac 1921gcctgcaagt ggtgcgagcg caggttcacg cagtccgggg acctgtacag acacattcgc 1981aagttccact gtgagttggt gaactccttg tcggtcaaaa gcgaagcact gagcttgcct 2041actgtcagag actggacctt agaagatagc tctcaagaac tttggaaata attttatata 2101tatataaata atatatatat atatacatat atataaatag atctctatat agttgtggta 2161cggtctaaaa gcagtcttgt ttcctggaaa taaaaagttg ggatattaac ttgtttttgc 2221actttagaat agcatgagaa tctcactaat ttagcattct gataaaagaa actttagagc 2281aagtcagaat agagaggtgt ttttcctttg aggggatagg ggaagtaagc caataagaac 2341cttttaaaca aatcgtcctg tcacaaaatg ctttcatatg gcttaatttt gtcaacactg 2401cattgtcttt tgagctcttt tttccccccc aacaaagttt ttttgttttt tgtttttttt 2461tttaagtaga aattccctcc agttttatta gcctctttat atgtctcaaa ttgcatgaat 2521tttttctggc tgttggaaac ctgaatgctt ttagacccaa atggaaaatt tctgaaatgc 2581tggattatct atttttaaac aagcagttga cttaaaactt tctgtggcaa cttctggttt 2641tctgacagtt cccagtgaga gaaatgctga aagtacactg ggatcactgg gacactgtct 2701tatgaaggtt tgcttgggat gaaaaaggat attgcagctt cagcagtgtt gaactgtgtg 2761tttaaaaatg tgaattactg ttattgtata ctgtaattga ttacatgggc tgggggggtg 2821tcaaagaact tgacaggttg tgttgatgct cttagttgag tcttgaaaag taaatattaa 2881cgctacagaa atgcatgagt ttcaatatat tttttgtctt tgtttgcatt gtataacttt 2941aacgagtgag tttaaaatta tttaatttcc ttagaaaaat agcaccattt ggaaaaaaaa 3001actggtgtta tgaagaacgt aaatgcactg tttttatttt tattttatat aatttaaatt 3061gactttccca ctgtctttaa gttgaaactg ttaagctgaa taaaaactta agctgcaaat 3121tgataacttc gctacataac aaggaaaata taaatgttta caaacagctt aaagatttgc 3181atgtgcagtg tgcatttata acaaacttct aattgcacaa aacccatgcc agctcagagt 3241ttaggtgtac acatttaccc agttgagcgt tcttagaata actactgcac aagttgacaa 3301taggtcgttc tctctttttt tttgtttgct ccctttttct ttttctcccc ttcctcctta 3361ccctccctcc cttactctcc ccccccacca ccaccctcca cccccaactc atgaaaagat 3421tctatggact gaaaaagccc caggctgaaa ggactggact gccttgattg acatggggaa 3481gggggttagt agactatgtg gattgcggca gcagaggctg cagcctaacg tgtggtttta 3541atgaccagca cgcaaggcaa aagcattttg cacagtgttt gttttcctgt cttgcactta 3601caaataaggt ctatgggagt agcatggaaa acgtttgctg tttttccctt ttttttttaa 3661ttgcttttgt ttaaaatttg atcgccttaa ctactgtaaa catagcctat ttttgtgctt 3721aagatactga atggaaaact ccattgtgtg ttgctggact gttttggaaa tatttggtta 3781aatgtgtgtt aatttggctg taatggcatt taaagcaaac aaacaaacaa aaaaagctgt 3841gaaaatggcc ttggagcatt atctttagtt acttgaagag tttctagttt ttttaaaata 3901cagtttatgt taaaataatt tttattaatt tagagaagac aatcaatgtc tgtgagaaaa 3961cggactttct tttggatttt ctttttgtgg tcattgtgag tgattgcttt ttccttttct 4021tagtttcaca ttcttccttt gttctaaaac ttagactgac atctagcttt gacaatcata 4081gtatgtttta ttttcctgag ggggaataac ttataatgct gtttagtttt gtactattgg 4141tgtgttggtg aatttttaaa ctgtgtgcta actgcaataa attatatgaa ctgagaaaaa 4201aaaaaaaaaa aaaaaaaaaa aaaaaaaaaa aaaaaaaaaa aaaa

Id Proteins.

Id (inhibitor of DNA binding or inhibitor of differentiation) proteinsbelong to the helix-loop-helix (HLH) protein superfamily that iscomposed of seven currently known subclasses. They function throughbinding and sequestration of basic HLH (bHLH) transcription factors,thus preventing DNA binding and transcriptional activation of targetgenes (Norton et al., 1998, Trends Cell Biol 8, 58-65; hereinincorporated by reference in its entirety). The dimerization of basicHLH proteins is necessary for their binding to DNA at the canonicalE-box (CANNTG; SEQ ID NO: 245) or N-box (CACNAG; SEQ ID NO: 246)recognition sequences. Id proteins lack the basic domain necessary forDNA binding, and act primarily as dominant-negative regulators of bHLHtranscription factors by sequestering and/or preventing DNA binding ofubiquitously expressed (e.g., E12, E47, E2-2) or cell-type-restricted(e.g., Tal-1, MyoD) factors. Four members of the Id protein family (Id1to Id4) have been identified in mammals. Id proteins share a highlyhomologous HLH region, but have divergent sequences elsewhere.

Id2 enhances cell proliferation by promoting the transition from G1 to Sphase of the cell cycle. Id proteins are abundantly expressed in stemcells, for example, neural stem cells before the decision to committowards distinct neural lineages (Iavarone and Lasorella, 2004, CancerLett 204, 189-196; Perk et al., 2005, Nat Rev Cancer 5, 603-614; eachherein incorporated by reference in its entirety). In stem cells, Idproteins act to maintain the undifferentiated and proliferativephenotype (Ying et al., 2003, Cell 115, 281-292; herein incorporated byreference in its entirety). Id expression is strongly reduced in maturecells from the central nervous system (CNS) but they accumulate at veryhigh levels in neural cancer (Iavarone and Lasorella, 2004, Cancer Lett204, 189-196; Lasorella et al., 2001, Oncogene 20, 8326-8333; eachherein incorporated by reference in its entirety).

Id proteins act as negative regulators of differentiation, and dependingon the specific cell lineage and developmental stage of the cell, Idproteins can act as positive regulators. Because bHLH proteins aremainly involved in the regulation of the expression of tissue specificand cell cycle related genes, Id-mediated sequestration or repression ofbHLH proteins serves to block differentiation and to promote cell cycleactivation. Accordingly, Id proteins have been shown to have biologicalroles as coordinators of different cellular processes, such as cell-fatedetermination, proliferation, cell-cycle regulation, angiogenesis, andcell migration. In some embodiments, the invention provides new methodsfor inhibiting proliferation of a neoplastic cell and for inhibitingangiogenesis in tumor tissue

The Biology of Human Malignant Brain Tumors.

High-grade gliomas, which include anaplastic astrocytoma (AA) andGlioblastoma Multiforme (GBM), are the most common intrinsic braintumors in adults and are almost invariably lethal, largely as a resultof their lack of responsiveness to current therapy (Legler et al., 200.J Natl Cancer Inst 92:77 A-8; herein incorporated by reference in itsentirety). High-grade gliomas are the most common brain tumors in humansand are essentially incurable (A4; herein incorporated by reference inits entirety). The biological features that confer aggressiveness tohuman glioma are tissue invasion, neo-vascularization, marked increasein proliferation and resistance to cell death. Just as the ability tometastasize identifies the highest degree of malignancy in epithelialtumors, the defining hallmarks of aggressiveness of glioblastomamultiforme (GBM) are local. invasion and neoangiogenesis (A5, A6; eachherein incorporated by reference in its entirety). Drivers of thesephenotypic traits include intrinsic autocrine signals produced by braintumor cells to invade the adjacent normal brain and stimulate formationof new blood vessels (A7; herein incorporated by reference in itsentirety). It has been suggested that GBM re-engages pre-establishedontogenetic motility and invasion signals that normally operate inneural stem cells and immature progenitors (A8; herein incorporated byreference in its entirety). A recently established notion postulatesthat neoplastic transformation in the central nervous system (CNS)converts neural stem cells into cell types manifesting a mesenchymalphenotype, a state associated with uncontrolled ability to invade andstimulate angiogenesis (A1, A2; each herein incorporated by reference inits entirety).

Differentiation along the mesenchymal lineage is virtually undetectablein the normal neural tissue during development. Global gene expressionstudies have established that over-expression of a “mesenchymal” geneexpression signature (MGES) and loss of a proneural signature (PGES)co-segregate with the poorest prognosis group of glioma patients (A1;herein incorporated by reference in its entirety) (for simplicity, wewill refer to the MGES+/PGES− signature as the mesenchymal phenotype ofhigh-grade gliomas). It is unclear whether drift towards the mesenchymallineage is exclusively an aberrant event that occurs during brain tumorprogression or whether glioma cells recapitulate the rare mesenchymalplasticity of neural stem cells (A1-3, A9; each herein incorporated byreference in its entirety). More importantly, the molecular events thattrigger activation/suppression of the MGES and PGES signatures andimpart an intrinsically aggressive phenotype to glioma cells remainunknown.

Accordingly, Gene Expression Profile (GEP) studies of malignant gliomaindicate that the expression of mesenchymal and angiogenesis-associatedgenes is associated with the worst prognosis (Freije, et al., 2004.Cancer Res 64:6503-10; Góddard et al., 2003. Cancer Res 63:6613-25;Liang et al., 2005. Proc Natl Acad Sci USA 102:5814-9; Nigro et al.,2005. Cancer Res 65:1678-86; each herein incorporated by reference inits entirety). Recently, glioma samples have been segregated into threegroups with distinctive GEP signatures, displaying expression of genescharacteristic of neural tissues (proneural), proliferating cells(proliferative) or mesenchymal tissues (mesenchymal) (Phillips et al.,2006. Cancer Cell 9:157-73; herein incorporated by reference in itsentirety). Malignant gliomas in the mesenchymal group express geneslinked with the most aggressive properties of GBM tumors (migration,invasion and angiogenesis) and invariably coincide with diseaserecurrence. The EXAMPLES discussed herein confirmed that molecularclassification of gliomas effectively predicts clinical outcome.However, a major open challenge is the mapping and modeling of theregulatory programs responsible for the differential regulation of thethree distinct expression signatures, each marking a specific cellularphenotype. In this proposal, we use combinations of computational andexperimental approaches to unravel and validate the transcriptional andpost translational interaction networks that drive the Mesenchymal GeneExpression Signature of high-grade glioma (MGES).

Maintenance of brain cells in a state referred to as “mesenchymal” isbelieved to be the cause of high-grade gliomas, the most common form ofbrain tumor in humans. For example, a pair of genes, Stat3 and C/EBPβ,can initiate and maintain the characteristics of the most commonhigh-grade gliomas. Stat3 and C/EBP/3 are both transcription factors,meaning that they regulate the function of other genes. In so doing,Stat3, and C/EBPβ are master regulators of the mesenchymal state ofbrain cells which is the signature of human glioma. Therefore they arepotential drug targets for the treatment of high-grade glioma. In someembodiments, co-expression of Stat3 and C/EBPβ in neural stem cells(brain cells that are naïve, otherwise called undifferentiated) issufficient to initiate expression of the mesenchymal set of genes,suppress proneural genes, and trigger invasion and a malignantmesenchymal phenotype in the mouse indicating that these two genes canbe causal for glioma. In some embodiments, silencing of these twotranscription factors depletes glioma stem cells and cell lines ofmesenchymal attributes and greatly impairs their ability to invade,perhaps indicating that silencing these genes help treat glioma. Asdiscussed in the examples herein, independent immunohistochemistryexperiments in 62 human glioma specimens show that concurrent expressionof Stat3 and C/EBP is significantly associated with the expression ofmesenchymal proteins and is an accurate predictor of poorest outcome inglioma patients.

In some embodiments, Stat3 and C/EBP are potential drug targets for thetreatment of high-grade gliomas, with either small-moleculepharmaceuticals or gene-therapy strategies such as interfering RNAs. Forexample, diagnostic procedures can be designed to take advantage of theknowledge that Stat3 and C/EBP are regulators of humanhigh-grade-gliomas. In some embodiments, measuring Stat3 and C/EBPexpression can be a predictor of poorest outcome in glioma patients.This can be used early as a diagnostic indicator for the development ofglioma.

Cell Regulatory Network Reverse Engineering.

Genome-scale approaches were recently applied to dissect regulatorynetworks in Eukaryotic organisms (Zhu et al., 2007. Genes Dev21:1010-24; herein incorporated by reference in its entirety). Thesestudies have shown that large-scale screens can be used to infermolecular interaction networks, with gene products represented as nodesand interactions as edges in a graph. Analysis of yeast networks(Barabasi and Oltavi, 2004. Nat Rev Genet. 5:101-13; herein incorporatedby reference in its entirety), further validated in a mammalian context(Basso et al., 2005. Nat Genet. 37:382-90; herein incorporated byreference in its entirety), revealed that a relatively small number ofkey genes (hubs) regulate a large number of interactions, generatingintense debate on the scale-free nature of these networks. Additionally,it has been shown that somatic lesions involved in tumorigenesis affectcentral hubs (Goh et al., 2007. Proc Natl Acad Sci USA 104:8685-90;herein incorporated by reference in its entirety). Master Regulators(MRs) are the regulatory hubs (transcriptional and post-translational)whose alteration is necessary and/or sufficient to implement a specificphenotypic transition (Lim et al., 2009. Pac Symp Biocomput 14:504-515;herein incorporated by reference in its entirety). Without being boundby theory, the combinatorial interaction of multiple, non-specific MRsyield high specificity in the control of individual programs associatedwith tumorigenesis and tumor aggressiveness. We thus plan to study therole of MRs and their combinatorial interplay in effecting the MGES thatconfers aggressiveness and recurrence to high-grade glioma.

The ARACNe and MINDy algorithms to reconstruct regulatory networksdriving the mesenchymal signature of high-grade glioma.

ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks)is an established approach for the reverse engineering oftranscriptional interactions from large GEP datasets (Basso et al.,2005. Nat Genet. 37:382-90; Margolin et al., 2006. BMC Bioinformatics 7Suppl 1:S7; each herein incorporated by reference in its entirety). Themain feature of this analytical tool is the use of the MutualInformation (MI) to identify candidate TF-target interactions. Indirectinteractions are eliminated using the Data Processing Inequality (DPI),a well-known theoretical property of MI. As shown in several publishedstudies and further demonstrated in the preliminary results section,ARACNe-inferred TF-target interactions have a high probability ofcorresponding to bona fide physical interactions. ARACNe was first usedto dissect transcriptional interactions in human B cells, withexperimental validation of C-MYC targets (Basso et al., 2005. Nat Genet.37:382-90; herein incorporated by reference in its entirety). Additionalstudies in T cells, peripheral leukocytes, and rat brain tissue haveconfirmed a 70% to 90% validation rate of the ARACNe inferred targetsfor a wide range of TFs by Chromatin ImmunoPrecipitation assays (ChIP)(Palomero et al., 2006. Proc Natl Acad Sci USA 103:18261-6; hereinincorporated by reference in its entirety). Software implementing ARACNewas downloaded by over 4,000 distinct researchers and has beenreferenced in ˜150 publications (Google Scholar), many of them providingindependent validation of the method. Two ARACNe publications wereselected by the Faculty of 1,000 (Basso et al., 2005. Nat Genet.37:382-90; Margolin et al., 2006. Nat Protoc 1:662-71; each hereinincorporated by reference in its entirety). Preliminary work using GBMmicroarray expression profile data (see EXAMPLES discussed herein) whereARACNe was developed indicates that the method is effective inheterogeneous cell populations. While cellular heterogeneity canincrease the number of interactions missed by the approach(false-negatives), it does not introduce incorrect interactions (falsepositives). This is addressed in the Preliminary Data section whereARACNe-inferred TFs-targets interactions in neural tissue are validated.

Modulator Inference by Network Dynamics (MINDy) is the first algorithmable to accurately infer genome-wide repertoires of post-translationalregulators of TF activity (Mani et al., 2008. Molecular Systems Biology4:169-179; Wang et al., 2009. Pacific Symposium on Biocomputing14:264-275; Wang et al., 2006. Lecture Notes in Computer Science3909:348-362; Wang, K., M. Saito, B. Bisikirska, M. Alvarez, W. K. Lim,P. Rajbhandari, Q. Shen, I. Nemenman, K. Basso, A. A. Margolin, U.Klein, R. Dalla Favera, and A. Califano. 2009. Genome-wideidentification of transcriptional network modulators in human B cells,Nature Biotechnology 27: 829-837; each herein incorporated by referencein its entirety). MINDy results have been used to infer (a) causallesions, (b) drug mechanism of action in hematopoietic malignancies(Mani et al., 2008. Molecular Systems Biology 4:169-179; hereinincorporated by reference in its entirety), and (c) to dissect theinterface between signaling and transcriptional processes in B cells(Wang et al., 2009. Pacific Symposium on Biocomputing 14:264-275; hereinincorporated by reference in its entirety). Inferences werebiochemically validated. See EXAMPLES 2-5 for further detail.

DNA and Amino Acid Manipulation Methods

The invention utilizes conventional molecular biology, microbiology, andrecombinant DNA techniques available to one of ordinary skill in theart. Such techniques are well known to the skilled worker and areexplained fully in the literature. See, e.g., Maniatis, Fritsch &Sambrook, “DNA Cloning: A Practical Approach,” Volumes I and II (D. N.Glover, ed., 1985); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984);“Nucleic Acid Hybridization” (B. D. Hames & S. J. Higgins, eds., 1985);“Transcription and Translation” (B. D. Hames & S. J. Higgins, eds.,1984); “Animal Cell Culture” (R. I. Freshney, ed., 1986); “ImmobilizedCells and Enzymes” (IRL Press, 1986): B. Perbal, “A Practical Guide toMolecular Cloning” (1984), and Sambrook, et al., “Molecular Cloning: aLaboratory Manual” (2001); herein incorporated by reference in itsentirety.

One skilled in the art can obtain aMesenchymal-Gene-Expression-Signature (MGES) protein or a variantthereof (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238), inseveral ways, which include, but are not limited to, isolating theprotein via biochemical means or expressing a nucleotide sequenceencoding the protein of interest by genetic engineering methods.

In another aspect, the invention provides for MGES molecule or variantsthereof that are encoded by nucleotide sequences. As used herein, a“MGES molecule” refers to a Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238 protein. The MGES molecule can be a polypeptideencoded by a nucleic acid (including genomic DNA, complementary DNA(cDNA), synthetic DNA, as well as any form of corresponding RNA). Forexample, a MGES molecule can be encoded by a recombinant nucleic acidencoding human MGES protein. The MGES molecules of the invention can beobtained from various sources and can be produced according to varioustechniques known in the art. For example, a nucleic acid that encodes aMGES molecule can be obtained by screening DNA libraries, or byamplification from a natural source. The MGES molecules of the inventioncan be produced via recombinant DNA technology and such recombinantnucleic acids can be prepared by conventional techniques, includingchemical synthesis, genetic engineering, enzymatic techniques, or acombination thereof. A MGES molecule of this invention can alsoencompasses variants of the human MGES proteins (e.g., Stat3, C/EBPβ,C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238). The variants can comprisenaturally-occurring variants due to allelic variations betweenindividuals (e.g., polymorphisms), mutated alleles related to hairgrowth or texture, or alternative splicing forms

In some embodiments, the nucleic acid is expressed in an expressioncassette, for example, to achieve overexpression in a cell. The nucleicacids of the invention can be an RNA, cDNA, cDNA-like, or a DNA ofinterest in an expressible format, such as an expression cassette, whichcan be expressed from the natural promoter or an entirely heterologouspromoter. The nucleic acid of interest can encode a protein, and may ormay not include introns.

Protein variants can involve amino acid sequence modifications. Forexample, amino acid sequence modifications fall into one or more ofthree classes: substitutional, insertional or deletional variants.Insertions can include amino and/or carboxyl terminal fusions as well asintrasequence insertions of single or multiple amino acid residues.Insertions ordinarily will be smaller insertions than those of amino orcarboxyl terminal fusions, for example, on the order of one to fourresidues. Deletions are characterized by the removal of one or moreamino acid residues from the protein sequence. These variants ordinarilyare prepared by site-specific mutagenesis of nucleotides in the DNAencoding the protein, thereby producing DNA encoding the variant, andthereafter expressing the DNA in recombinant cell culture.

Techniques for making substitution mutations at predetermined sites inDNA having a known sequence are well known, for example M13 primermutagenesis and PCR mutagenesis. Amino acid substitutions can be singleresidues, but can occur at a number of different locations at once. Insome non-limiting embodiments, insertions can be on the order of aboutfrom 1 to about 10 amino acid residues, while deletions can range fromabout 1 to about 30 residues. Deletions or insertions can be made inadjacent pairs (for example, a deletion of about 2 residues or insertionof about 2 residues). Substitutions, deletions, insertions, or anycombination thereof can be combined to arrive at a final construct. Themutations cannot place the sequence out of reading frame and cannotcreate complementary regions that can produce secondary mRNA structure.Substitutional variants are those in which at least one residue has beenremoved and a different residue inserted in its place.

Expression Systems

Bacterial and Yeast Expression Systems.

In bacterial systems, a number of expression vectors can be selected.For example, when a large quantity of an MGES protein is needed for theinduction of antibodies, vectors which direct high level expression offusion proteins that are readily purified can be used. Non-limitingexamples of such vectors include multifunctional E. coli cloning andexpression vectors such as BLUESCRIPT (Stratagene). pIN vectors or pGEXvectors (Promega, Madison, Wis.) also can be used to express foreignpolypeptide molecules as fusion proteins with glutathione S-transferase(GST). In general, such fusion proteins are soluble and can easily bepurified from lysed cells by adsorption to glutathione-agarose beadsfollowed by elution in the presence of free glutathione. Proteins madein such systems can be designed to include heparin, thrombin, or factorXa protease cleavage sites so that the cloned polypeptide of interestcan be released from the GST moiety at will.

Plant and Insect Expression Systems.

If plant expression vectors are used, the expression of sequencesencoding a MGES molecule can be driven by any of a number of promoters.For example, viral promoters such as the 35S and 19S promoters of CaMVcan be used alone or in combination with the omega leader sequence fromTMV. Alternatively, plant promoters such as the small subunit of RUBISCOor heat shock promoters, can be used. These constructs can be introducedinto plant cells by direct DNA transformation or by pathogen-mediatedtransfection.

An insect system also can be used to express MGES molecules. Forexample, in one such system Autographa californica nuclear polyhedrosisvirus (AcNPV) is used as a vector to express foreign genes in Spodopterafrugiperda cells or in Trichoplusia larvae. Sequences encoding a MGESmolecule can be cloned into a non-essential region of the virus, such asthe polyhedrin gene, and placed under control of the polyhedrinpromoter. Successful insertion of MGES nucleic acid sequences willrender the polyhedrin gene inactive and produce recombinant viruslacking coat protein. The recombinant viruses can then be used to infectS. frugiperda cells or Trichoplusia larvae in which MGES or a variantthereof can be expressed.

Mammalian Expression Systems.

An expression vector can include a nucleotide sequence that encodes aMGES molecule linked to at least one regulatory sequence in a mannerallowing expression of the nucleotide sequence in a host cell. A numberof viral-based expression systems can be used to express a MGES moleculeor a variant thereof in mammalian host cells. The vector can be arecombinant DNA or RNA vector, and includes DNA plasmids or viralvectors. For example, if an adenovirus is used as an expression vector,sequences encoding a MGES molecule can be ligated into an adenovirustranscription/translation complex comprising the late promoter andtripartite leader sequence. Insertion into a non-essential E1 or E3region of the viral genome can be used to obtain a viable virus which iscapable of expressing a MGES molecule in infected host cells.Transcription enhancers, such as the Rous sarcoma virus (RSV) enhancer,can also be used to increase expression in mammalian host cells. Inaddition, a multitargeting interfering RNA molecule expressing viralvectors can be constructed based on, but not limited to,adeno-associated virus, retrovirus, adenovirus, lentivirus oralphavirus.

Regulatory sequences are well known in the art, and can be selected todirect the expression of a protein or polypeptide of interest (such as aMGES molecule) in an appropriate host cell as described in Goeddel, GeneExpression Technology: Methods in Enzymology 185, Academic Press, SanDiego, Calif. (1990); herein incorporated by reference in its entirety.Non-limiting examples of regulatory sequences include: polyadenylationsignals, promoters (such as CMV, ASV, SV40, or other viral promoterssuch as those derived from bovine papilloma, polyoma, and Adenovirus 2viruses (Fiers, et al., 1973, Nature 273:113; Hager G L, et al., CurrOpin Genet Dev, 2002, 12(2):137-41; each herein incorporated byreference in its entirety) enhancers, and other expression controlelements.

Enhancer regions, which are those sequences found upstream or downstreamof the promoter region in non-coding DNA regions, are also known in theart to be important in optimizing expression. If needed, origins ofreplication from viral sources can be employed, such as if a prokaryotichost is utilized for introduction of plasmid DNA. However, in eukaryoticorganisms, chromosome integration is a common mechanism for DNAreplication.

For stable transfection of mammalian cells, a small fraction of cellscan integrate introduced DNA into their genomes. The expression vectorand transfection method utilized can be factors that contribute to asuccessful integration event. For stable amplification and expression ofa desired protein, a vector containing DNA encoding a protein ofinterest (for example, a P2RY5 molecule) is stably integrated into thegenome of eukaryotic cells (for example mammalian cells, such as cellsfrom the end bulb of the hair follicle), resulting in the stableexpression of transfected genes. An exogenous nucleic acid sequence canbe introduced into a cell (such as a mammalian cell, either a primary orsecondary cell) by homologous recombination as disclosed in U.S. Pat.No. 5,641,670, the entire contents of which are herein incorporated byreference.

A gene that encodes a selectable marker (for example, resistance toantibiotics or drugs, such as ampicillin, neomycin, G418, andhygromycin) can be introduced into host cells along with the gene ofinterest in order to identify and select clones that stably express agene encoding a protein of interest. The gene encoding a selectablemarker can be introduced into a host cell on the same plasmid as thegene of interest or can be introduced on a separate plasmid. Cellscontaining the gene of interest can be identified by drug selectionwherein cells that have incorporated the selectable marker gene willsurvive in the presence of the drug. Cells that have not incorporatedthe gene for the selectable marker die. Surviving cells can then bescreened for the production of the desired protein molecule (forexample, a MGES protein).

Cell Transfection and Culturing

Cell Transfection.

A eukaryotic expression vector can be used to transfect cells in orderto produce proteins (for example, a MGES molecule) encoded by nucleotidesequences of the vector. Mammalian cells can contain an expressionvector (for example, one that contains a gene encoding a MGES molecule)via introducing the expression vector into an appropriate host cell viamethods known in the art.

A host cell strain can be chosen for its ability to modulate theexpression of the inserted sequences or to process the expressed MGESpolypeptide (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, ZNF238) in the desired fashion. Such modifications of thepolypeptide include, but are not limited to, acetylation, carboxylation,glycosylation, phosphorylation, lipidation, and acylation.Post-translational processing which cleaves a “prepro” form of thepolypeptide also can be used to facilitate correct insertion, foldingand/or function. Different host cells which have specific cellularmachinery and characteristic mechanisms for post-translationalactivities (e.g., CHO, HeLa, MDCK, HEK293, and WI38), are available fromthe American Type Culture Collection (ATCC; University Boulevard,Manassas, Va. 20110-2209) and can be chosen to ensure the correctmodification and processing of the foreign protein.

An exogenous nucleic acid can be introduced into a cell via a variety oftechniques known in the art, such as lipofection, microinjection,calcium phosphate or calcium chloride precipitation,DEAE-dextrin-mediated transfection, or electroporation. Electroporationis carried out at approximate voltage and capacitance to result in entryof the DNA construct(s) into cells of interest (such as cells of the endbulb of a hair follicle, for example dermal papilla cells or dermalsheath cells). Other methods used to transfect cells can also includemodified calcium phosphate precipitation, polybrene precipitation,liposome fusion, and receptor-mediated gene delivery.

Cells to be genetically engineered can be primary and secondary cellsobtained from various tissues, and include cell types which can bemaintained and propagated in culture. Non-limiting examples of primaryand secondary cells include epithelial cells, neural cells, endothelialcells, glial cells, fibroblasts, muscle cells (such as myoblasts)keratinocytes, formed elements of the blood (e.g., lymphocytes, bonemarrow cells), and precursors of these somatic cell types. Vertebratetissue can be obtained by methods known to one skilled in the art, sucha punch biopsy or other surgical methods of obtaining a tissue source ofthe primary cell type of interest. A mixture of primary cells can beobtained from the tissue, using methods readily practiced in the art,such as explanting or enzymatic digestion (for examples using enzymessuch as pronase, trypsin, collagenase, elastase dispase, andchymotrypsin). Biopsy methods have also been described in United StatesPatent Application Publication 2004/0057937 and PCT applicationpublication WO 2001/32840, each of which are hereby incorporated byreference in its entirety.

Primary cells can be acquired from the individual to whom thegenetically engineered primary or secondary cells are administered.However, primary cells can also be obtained from a donor, other than therecipient, of the same species. The cells can also be obtained fromanother species (for example, rabbit, cat, mouse, rat, sheep, goat, dog,horse, cow, bird, or pig). Primary cells can also include cells from anisolated vertebrate tissue source grown attached to a tissue culturesubstrate (for example, flask or dish) or grown in a suspension; cellspresent in an explant derived from tissue; both of the aforementionedcell types plated for the first time; and cell culture suspensionsderived from these plated cells. Secondary cells can be plated primarycells that are removed from the culture substrate and replated, orpassaged, in addition to cells from the subsequent passages. Secondarycells can be passaged one or more times. These primary or secondarycells can contain expression vectors having a gene that encodes aprotein of interest (for example, a MGES molecule).

Cell Culturing.

Various culturing parameters can be used with respect to the host cellbeing cultured. Appropriate culture conditions for mammalian cells arewell known in the art (Cleveland W L, et al., J Immunol Methods, 1983,56(2): 221-234; herein incorporated by reference in its entirety) or canbe determined by the skilled artisan (see, for example, Animal CellCulture: A Practical Approach 2nd Ed., Rickwood, D. and Hames, B. D.,eds. (Oxford University Press: New York, 1992); herein incorporated byreference in its entirety). Cell culturing conditions can vary accordingto the type of host cell selected. Commercially available medium can beutilized. Non-limiting examples of medium include, for example, MinimalEssential Medium (MEM, Sigma, St. Louis, Mo.); Dulbecco's ModifiedEagles Medium (DMEM, Sigma); Ham's FIO Medium (Sigma); HyClone cellculture medium (HyClone, Logan, Utah); RPMI-1640 Medium (Sigma); andchemically-defined (CD) media, which are formulated for various celltypes, e.g., CD-CHO Medium (Invitrogen, Carlsbad, Calif.).

The cell culture media can be supplemented as necessary withsupplementary components or ingredients, including optional components,in appropriate concentrations or amounts, as necessary or desired. Cellculture medium solutions provide at least one component from one or moreof the following categories: (1) an energy source, usually in the formof a carbohydrate such as glucose; (2) all essential amino acids, andusually the basic set of twenty amino acids plus cysteine; (3) vitaminsand/or other organic compounds required at low concentrations; (4) freefatty acids or lipids, for example linoleic acid; and (5) traceelements, where trace elements are defined as inorganic compounds ornaturally occurring elements that can be required at very lowconcentrations, usually in the micromolar range.

The medium also can be supplemented electively with one or morecomponents from any of the following categories: (1) salts, for example,magnesium, calcium, and phosphate; (2) hormones and other growth factorssuch as, serum, insulin, transferrin, and epidermal growth factor; (3)protein and tissue hydrolysates, for example peptone or peptone mixtureswhich can be obtained from purified gelatin, plant material, or animalbyproducts; (4) nucleosides and bases such as, adenosine, thymidine, andhypoxanthine; (5) buffers, such as HEPES; (6) antibiotics, such asgentamycin or ampicillin; (7) cell protective agents, for examplepluronic polyol; and (8) galactose. In some embodiments, soluble factorscan be added to the culturing medium.

Cells suitable for culturing can contain introduced expression vectors,such as plasmids or viruses. The expression vector constructs can beintroduced via transformation, microinjection, transfection,lipofection, electroporation, or infection. The expression vectors cancontain coding sequences, or portions thereof, encoding the proteins forexpression and production. Expression vectors containing sequencesencoding the produced proteins and polypeptides, as well as theappropriate transcriptional and translational control elements, can begenerated using methods well known to and practiced by those skilled inthe art. These methods include synthetic techniques, in vitrorecombinant DNA techniques, and in vivo genetic recombination which aredescribed in J. Sambrook et al., 201, Molecular Cloning, A LaboratoryManual, Cold Spring Harbor Press, Cold Spring Harbor, N.Y. and in F. M.Ausubel et al., 1989, Current Protocols in Molecular Biology, John Wiley& Sons, New York, N.Y.; each herein incorporated by reference in itsentirety.

DNA and Polypeptides, Methods, and Purification Thereof

The present invention utilizes conventional molecular biology,microbiology, and recombinant DNA techniques available to one ofordinary skill in the art. Such techniques are well known to the skilledworker and are explained fully in the literature. See, e.g. “DNACloning: A Practical Approach,” Volumes 1 and II (D. N. Glover, ed.,1985); “Oligonucleotide Synthesis” (M. J. Gait, ed., 1984); “NucleicAcid Hybridization” (B. D. Hames & S. J. Higgins, eds., 1985);“Transcription and Translation” (B. D. Hames & S. J. Higgins, eds.,1984); “Animal Cell Culture” (R. I. Freshney, ed., 1986); “ImmobilizedCells and Enzymes” (IRL Press, 1986): B. Perbal, “A Practical Guide toMolecular Cloning” (1984), and Sambrook, et al., “Molecular Cloning: aLaboratory Manual” (3^(rd) edition, 2001); each herein incorporated byreference in its entirety. One skilled in the art can obtain a proteinencoded by an MGES gene (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1,FosL2, bHLH-B2, ZNF238) in several ways, which include, but are notlimited to, isolating the protein via biochemical means or expressing anucleotide sequence encoding the protein of interest by geneticengineering methods. For example, Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, ZNF238, or a variant thereof, can be obtained by purifying itfrom human cells expressing the same, or by direct chemical synthesis.

Host cells which contain a nucleic acid encoding an MGES polypeptide(such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238),and which subsequently express a protein encoded by an MGES gene (suchas, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238), can beidentified by various procedures known to those of skill in the art.These procedures include, but are not limited to, DNA-DNA or DNA-RNAhybridizations and protein bioassay or immunoassay techniques whichinclude membrane, solution, or chip-based technologies for the detectionand/or quantification of nucleic acid or protein. For example, thepresence of a nucleic acid encoding a MGES polypeptide (such as, e.g.,Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238) can be detected byDNA-DNA or DNA-RNA hybridization or amplification using probes orfragments of nucleic acids encoding a MGES polypeptide.

Amplification methods include, e.g., polymerase chain reaction, PCR (PCRPROTOCOLS, A GUIDE TO METHODS AND APPLICATIONS, ed. Innis, AcademicPress, N.Y., 1990 and PCR STRATEGIES, 1995, ed. Innis, Academic Press,Inc., N.Y.; each herein incorporated by reference in its entirety),ligase chain reaction (LCR) (see, e.g., Wu, Genomics 4:560, 1989;Landegren, Science 241:1077, 1988; Barringer, Gene 89:117, 1990; eachherein incorporated by reference in its entirety); transcriptionamplification (see, e.g., Kwoh, Proc. Natl. Acad. Sci. USA 86:1173,1989; herein incorporated by reference in its entirety); and,self-sustained sequence replication (see, e.g., Guatelli, Proc. Natl.Acad. Sci. USA 87:1874, 1990; herein incorporated by reference in itsentirety); Q Beta replicase amplification (see, e.g., Smith, J. Clin.Microbiol. 35:1477-1491, 1997; herein incorporated by reference in itsentirety), automated Q-beta replicase amplification assay (see, e.g.,Burg, Mol. Cell. Probes 10:257-271, 1996; herein incorporated byreference in its entirety) and other RNA polymerase mediated techniques(e.g., NASBA, Cangene, Mississauga, Ontario); see also Berger, MethodsEnzymol. 152:307-316, 1987; U.S. Pat. Nos. 4,683,195 and 4,683,202;Sooknanan, Biotechnology 13:563-564, 1995; each herein incorporated byreference in its entirety.

A guide to the hybridization of nucleic acids is found in e.g.,Sambrook, ed., Molecular Cloning: A Laboratory Manual (3^(rd) Ed.),Vols. 1-3, Cold Spring Harbor Laboratory, 2001; Current Protocols InMolecular Biology, Ausubel, ed. John Wiley & Sons, Inc., New York, 1997;Laboratory Techniques In Biochemistry And Molecular Biology:Hybridization With Nucleic Acid Probes, Part I. Theory and Nucleic AcidPreparation, Tijssen, ed. Elsevier, N.Y., 1993; each herein incorporatedby reference in its entirety.

In some embodiments, a fragment of a nucleic acid of an MGES gene (suchas, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238) canencompass any portion of at least about 8 consecutive nucleotides ofeither SEQ ID NOS: 232, 234, 236, 238, 240, 242, or 244. In someembodiments, the fragment can comprise at least about 10 consecutivenucleotides, at least about 15 consecutive nucleotides, at least about20 consecutive nucleotides, or at least about 30 consecutive nucleotidesof either SEQ ID NOS: 232, 234, 236, 238, 240, 242, or 244. Fragmentscan include all possible nucleotide lengths between about 8 and about100 nucleotides, for example, lengths between about 15 and about 100nucleotides, or between about 20 and about 100 nucleotides. Nucleic acidamplification-based assays involve the use of oligonucleotides selectedfrom sequences encoding a polypeptide encoded by an MGES gene (such as,e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238), to detecttransformants which contain a nucleic acid encoding an MGES protein orpolypeptide, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238.

Various techniques known in the art can be used to detect or quantifyaltered gene expression, RNA expression, or sequence, which include, butare not limited to, hybridization, sequencing, amplification, and/orbinding to specific ligands (such as antibodies). Other suitable methodsinclude allele-specific oligonucleotide (ASO), oligonucleotide ligation,allele-specific amplification, Southern blot (for DNAs), Northern blot(for RNAs), single-stranded conformation analysis (SSCA), PFGE,fluorescent in situ hybridization (FISH), gel migration, clampeddenaturing gel electrophoresis, denaturing HLPC, melting curve analysis,heteroduplex analysis, RNase protection, chemical or enzymatic mismatchcleavage, ELISA, radio-immunoassays (RIA) and immuno-enzymatic assays(IEMA). Some of these approaches (such as SSCA and CGGE) are based on achange in electrophoretic mobility of the nucleic acids, as a result ofthe presence of an altered sequence. According to these techniques, thealtered sequence is visualized by a shift in mobility on gels. Thefragments can then be sequenced to confirm the alteration. Some otherapproaches are based on specific hybridization between nucleic acidsfrom the subject and a probe specific for wild type or altered gene orRNA. The probe can be in suspension or immobilized on a substrate. Theprobe can be labeled to facilitate detection of hybrids. Some of theseapproaches are suited for assessing a polypeptide sequence or expressionlevel, such as Northern blot, ELISA and RIA. These latter require theuse of a ligand specific for the polypeptide, for example, the use of aspecific antibody.

Embodiments of the invention provide for detecting whether expression ofan MGES gene (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238) is altered. In some embodiments, the gene alterationcan result in increased or reduced gene expression and/or activity. Insome embodiments, the gene alteration can also result in increased orreduced protein expression and/or activity.

An alteration in a MGES gene locus (e.g., where Stat3, C/EBPβ, C/EBPδ,RunX1, FosL2, bHLH-B2, or ZNF238 are located) can be any form ofmutation(s), deletion(s), rearrangement(s) and/or insertions in thecoding and/or non-coding region of the locus, alone or in variouscombination(s). Mutations can include point mutations. Insertions canencompass the addition of one or several residues in a coding ornon-coding portion of the gene locus. Insertions can comprise anaddition of between 1 and 50 base pairs in the gene locus. Deletions canencompass any region of one, two or more residues in a coding ornon-coding portion of the gene locus, such as from two residues up tothe entire gene or locus. Deletions can affect smaller regions, such asdomains (introns) or repeated sequences or fragments of less than about50 consecutive base pairs; although larger deletions can occur as well.Rearrangement includes inversion of sequences.

The MGES gene locus alteration (such as, e.g., Stat3, C/EBPβ, C/EBPδ,RunX1, FosL2, bHLH-B2, or ZNF238) can result in amino acidsubstitutions, RNA splicing or processing, product instability, thecreation of stop codons, frame-shift mutations, and/or truncatedpolypeptide production. The alteration can result in the production of aMGES polypeptide with altered function, stability, targeting orstructure. The alteration can also cause a reduction in proteinexpression. In some embodiments, the alteration in a MGES gene locus cancomprise a point mutation, a deletion, or an insertion in the MGES geneor corresponding expression product. The alteration can be determined atthe level of the DNA, RNA, or polypeptide.

In some embodiments, the detecting comprises detecting in a biologicalsample whether there is a reduction in an mRNA encoding an MGESpolypeptide, or a reduction in a MGES protein, or a combination thereof.In some embodiments, the detecting comprises detecting in a biologicalsample whether there is a reduction in an mRNA encoding an MGESpolypeptide, or a reduction in a MGES protein, or a combination thereof.The presence of such an alteration is indicative of the presence orpredisposition to a nervous system cancer (e.g., a glioma). The presenceof an alteration in an MGES gene encoding an MGES polypeptide (such as,e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238) in thesample is detected through the genotyping of a sample, for example viagene sequencing, selective hybridization, amplification, gene expressionanalysis, or a combination thereof.

Methods for detecting and quantifying MGES polypeptides (such as, e.g.,Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238 polypeptides)and MGES polynucleotides (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238 polynucleotides) in biological samples are known theart. For example, protocols for detecting and measuring the expressionof a polypeptide encoded by an MGES gene, such as Stat3, C/EBPβ, C/EBPδ,RunX1, FosL2, bHLH-B2, or ZNF238, using either polyclonal or monoclonalantibodies specific for the polypeptide are well established.Non-limiting examples include enzyme-linked immunosorbent assay (ELISA),radioimmunoassay (RIA), and fluorescence activated cell sorting (FACS).A two-site, monoclonal-based immunoassay using monoclonal antibodiesreactive to two non-interfering epitopes on a polypeptide encoded by anMGES gene (e.g, Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238)can be used, or a competitive binding assay can be employed. In someembodiments, expression or over-expression of an MGES gene product(e.g., a MGES polypeptide or MGES mRNA) can be determined. In someembodiments, a biological sample comprises, a blood sample, serum, cells(including whole cells, cell fractions, cell extracts, and culturedcells or cell lines), tissues (including tissues obtained by biopsy),body fluids (e.g., urine, sputum, amniotic fluid, synovial fluid), orfrom media (from cultured cells or cell lines). The methods of detectingor quantifying MGES polynucleotides (such as, e.g., Stat3, C/EBPβ,C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238) include, but are not limitedto, amplification-based assays with signal amplification) hybridizationbased assays and combination amplification-hybridization assays. Fordetecting and quantifying MGES polypeptides (such as, e.g., Stat3,C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238), an exemplary methodis an immunoassay that utilizes an antibody or other binding agents thatspecifically bind to a MGES polypeptide (such as, e.g., Stat3, C/EBPβ,C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238) or epitope of such, forexample, ELISA or RIA assays.

Labeling and conjugation techniques are known by those skilled in theart and can be used in various nucleic acid and amino acid assays.Methods for producing labeled hybridization or PCR probes for detectingsequences related to nucleic acid sequences encoding an MGES protein(such as, e.g., Stat3, C/EBPβ, C/EBPβ, RunX1, FosL2, bHLH-B2, orZNF238), include, but are not limited to, oligolabeling, nicktranslation, end-labeling, or PCR amplification using a labelednucleotide. Alternatively, a nucleic acid sequence encoding apolypeptide encoded by an MGES gene can be cloned into a vector for theproduction of an mRNA probe. Such vectors are known in the art, arecommercially available, and can be used to synthesize RNA probes invitro by addition of labeled nucleotides and an appropriate RNApolymerase such as T7, T3, or SP6. These procedures can be conductedusing a variety of commercially available kits (Amersham PharmaciaBiotech, Promega, and US Biochemical). Suitable reporter molecules orlabels which can be used for ease of detection include radionuclides,enzymes, and fluorescent, chemiluminescent, or chromogenic agents, aswell as substrates, cofactors, inhibitors, and/or magnetic particles.

Host cells transformed with a nucleic acid sequence encoding an MGESpolypeptide (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238), can be cultured under conditions suitable for theexpression and recovery of the protein from cell culture. Thepolypeptide produced by a transformed cell can be secreted or containedintracellularly depending on the sequence and/or the vector used.Expression vectors containing a nucleic acid sequence encoding an MGESpolypeptide can be designed to contain signal sequences which directsecretion of soluble polypeptide molecules encoded by an MGES gene (suchas, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238),through a prokaryotic or eukaryotic cell membrane, or which direct themembrane insertion of a membrane-bound polypeptide molecule encoded byan MGES gene.

Other constructions can also be used to join a gene sequence encoding anMGES polypeptide (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238) to a nucleotide sequence encoding a polypeptidedomain which would facilitate purification of soluble proteins. Suchpurification facilitating domains include, but are not limited to, metalchelating peptides such as histidine-tryptophan modules that allowpurification on immobilized metals, protein A domains that allowpurification on immobilized immunoglobulin, and the domain utilized inthe FLAGS extension/affinity purification system (Immunex Corp.,Seattle, Wash.). Including cleavable linker sequences (i.e., thosespecific for Factor Xa or enterokinase (Invitrogen, San Diego, Calif.))between the purification domain and a polypeptide encoded by an MGESgene also can be used to facilitate purification. One such expressionvector provides for expression of a fusion protein containing apolypeptide encoded by an MGES gene (such as, e.g., Stat3, C/EBPβ,C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238) and 6 histidine residuespreceding a thioredoxin or an enterokinase cleavage site. The histidineresidues facilitate purification by immobilized metal ion affinitychromatography, while the enterokinase cleavage site provides a meansfor purifying the polypeptide encoded by an MGES gene.

An MGES polypeptide (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, or ZNF238) can be purified from any human or non-human cellwhich expresses the polypeptide, including those which have beentransfected with expression constructs that express an MGES protein. Apurified MGES polypeptide (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1,FosL2, bHLH-B2, or ZNF238) can be separated from other compounds whichnormally associate with the MGES polypeptide in the cell, such ascertain proteins, carbohydrates, or lipids, using methods practiced inthe art. Non-limiting methods include size exclusion chromatography,ammonium sulfate fractionation, affinity chromatography, ion exchangechromatography, and preparative gel electrophoresis.

Nucleic acid sequences comprising an MGES gene (such as, e.g., Stat3,C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238) that encode apolypeptide can be synthesized, in whole or in part, using chemicalmethods known in the art. Alternatively, an MGES polypeptide can beproduced using chemical methods to synthesize its amino acid sequence,such as by direct peptide synthesis using solid-phase techniques.Protein synthesis can either be performed using manual techniques or byautomation. Automated synthesis can be achieved, for example, usingApplied Biosystems 431 A Peptide Synthesizer (Perkin Elmer). Optionally,fragments of MGES polypeptides can be separately synthesized andcombined using chemical methods to produce a full-length molecule. Insome embodiments, a fragment of a nucleic acid sequence that comprisesan MGES gene (such as, e.g., Stat3, C/EBPβ, C/EBPβ, RunX1, FosL2,bHLH-B2, or ZNF238) can encompass any portion of at least about 8consecutive nucleotides of SEQ ID NO: 232, 234, 236, 238, 240, 242, or244. In some embodiments, the fragment can comprise at least about 10nucleotides, at least about 15 nucleotides, at least about 20nucleotides, or at least about 30 nucleotides of SEQ ID NO: 232, 234,236, 238, 240, 242, or 244. Fragments include all possible nucleotidelengths between about 8 and about 100 nucleotides, for example, lengthsbetween about 15 and about 100 nucleotides, or between about 20 andabout 100 nucleotides.

An MGES fragment can be a fragment of an MGES protein, such as, e.g.,Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, and ZNF238. For example,the MGES fragment can encompass any portion of at least about 8consecutive amino acids of SEQ ID NO: 231, 233, 235, 237, 239, 241, or243. The fragment can comprise at least about 10 consecutive aminoacids, at least about 20 consecutive amino acids, at least about 30consecutive amino acids, at least about 40 consecutive amino acids, aleast about 50 consecutive amino acids, at least about 60 consecutiveamino acids, at least about 70 consecutive amino acids, or at leastabout 75 consecutive amino acids of SEQ ID NO: 231, 233, 235, 237, 239,241, or 243. Fragments include all possible amino acid lengths betweenabout 8 and 100 about amino acids, for example, lengths between about 10and about 100 amino acids, between about 15 and about 100 amino acids,between about 20 and about 100 amino acids, between about 35 and about100 amino acids, between about 40 and about 100 amino acids, betweenabout 50 and about 100 amino acids, between about 70 and about 100 aminoacids, between about 75 and about 100 amino acids, or between about 80and about 100 amino acids.

A synthetic peptide can be substantially purified via high performanceliquid chromatography (HPLC). The composition of a synthetic MGESpolypeptide can be confirmed by amino acid analysis or sequencing.Additionally, any portion of an amino acid sequence comprising a proteinencoded by an MGES gene (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, and ZNF238) can be altered during direct synthesis and/orcombined using chemical methods with sequences from other proteins toproduce a variant polypeptide or a fusion protein.

The invention further encompasses methods for using a protein orpolypeptide encoded by a nucleic acid sequence of an MGES gene, such asthe sequences shown in SEQ ID NOS: 231, 233, 235, 237, 239, 241, or 244.In some embodiments, the polypeptide can be modified, such as byglycosylations and/or acetylations and/or chemical reaction or coupling,and can contain one or several non-natural or synthetic amino acids. Anexample of an MGES polypeptide has the amino acid sequence shown ineither SEQ ID NO: 231, 233, 235, 237, 239, 241, or 244. In certainembodiments, the invention encompasses variants of a human proteinencoded by an MGES gene (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1,FosL2, bHLH-B2, and ZNF238). Such variants can include those having atleast from about 46% to about 50% identity to SEQ ID NO: 231, 233, 235,237, 239, 241, or 244, or having at least from about 50.1% to about 55%identity to SEQ ID NO: 231, 233, 235, 237, 239, 241, or 244, or havingat least from about 55.1% to about 60% identity to SEQ ID NO: 231, 233,235, 237, 239, 241, or 244, or having from at least about 60.1% to about65% identity to SEQ ID NO: 231, 233, 235, 237, 239, 241, or 244, orhaving from about 65.1% to about 70% identity to SEQ ID NO: 231, 233,235, 237, 239, 241, or 244, or having at least from about 70.1% to about75% identity to SEQ ID NO: 231, 233, 235, 237, 239, 241, or 244, orhaving at least from about 75.1% to about 80% identity to SEQ ID NO:231, 233, 235, 237, 239, 241, or 244, or having at least from about80.1% to about 85% identity to SEQ ID NO: 231, 233, 235, 237, 239, 241,or 244, or having at least from about 85.1% to about 90% identity to SEQID NO: 231, 233, 235, 237, 239, 241, or 244, or having at least fromabout 90.1% to about 95% identity to SEQ ID NO 231, 233, 235, 237, 239,241, or 244, or having at least from about 95.1% to about 97% identityto SEQ ID NO: 231, 233, 235, 237, 239, 241, or 244, or having at leastfrom about 97.1% to about 99% identity to SEQ ID NO: 231, 233, 235, 237,239, 241, or 244.

Identifying MGES Modulating Compounds

The invention provides methods for identifying compounds which can beused for controlling and/or regulating mesenchymal signature genes(i.e., MGES genes such as Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2,ZNF238) of nervous system cancers. In addition, the invention providesmethods for identifying compounds which can be used for the treatment ofa nervous system cancers, such as malignant glioma. The methods cancomprise the identification of test compounds or agents (e.g., peptides(such as antibodies or fragments thereof), small molecules, nucleicacids (such as siRNA or antisense RNA), or other agents) that can bindto a MGES polypeptide molecule and/or have a stimulatory or inhibitoryeffect on the biological activity of MGES or its expression, andsubsequently determining whether these compounds can regulatemesenchymal signature genes of nervous system cancers in a subject orcan have an effect on tumor growth in an in vitro or an in vivo assay(i.e., examining whether there is a decrease in tumor growth).

As used herein, a “MGES modulating compound” refers to a compound thatinteracts with an MGES transcription factor and modulates its DNAbinding activity and/or its expression. The compound can either increasea MGES' activity or expression (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1,FosL2, bHLH-B2, ZNF238). Conversely, the compound can decrease a MGES'activity or expression (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, ZNF238). The compound can be a MGES inhibitor, agonist, or aMGES antagonist. Some non-limiting examples of MGES modulating compoundsinclude peptides (such as MGES peptide fragments, or antibodies orfragments thereof), small molecules, and nucleic acids (such as MGESsiRNA or antisense RNA specific for a MGES nucleic acid). Agonists of aMGES molecule can be molecules which, when bound to a MGES (such asStat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238) increase theexpression, or increase or prolong the activity of a MGES molecule.Agonists of a MGES include, but are not limited to, proteins, nucleicacids, small molecules, or any other molecule which activates MGES.Antagonists of a MGES molecule can be molecules which, when bound toMGES or a variant thereof, decrease the amount or the duration of theactivity of a MGES molecule. Antagonists include proteins, nucleicacids, antibodies, small molecules, or any other molecule which decreasethe activity of MGES.

The term “modulate”, as it appears herein, refers to a change in theactivity or expression of a MGES molecule (such as, Stat3, C/EBPβ,C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238). For example, modulation cancause an increase or a decrease in protein activity, bindingcharacteristics, or any other biological, functional, or immunologicalproperties of a MGES molecule.

In some embodiments, a MGES modulating compound can be a peptidefragment of a MGES protein that binds to the MGES or the upstream DNAregion where the MGES transcription factor binds to. Peptide fragmentscan be obtained commercially or synthesized via liquid phase or solidphase synthesis methods (Atherton et al., (1989) Solid Phase PeptideSynthesis: a Practical Approach. IRL Press, Oxford, England; hereinincorporated by reference in its entirety). The MGES peptide fragmentscan be isolated from a natural source, genetically engineered, orchemically prepared. These methods are well known in the art.

A MGES modulating compound can also be a protein, such as an antibody(monoclonal, polyclonal, humanized, and the like), or a binding fragmentthereof, directed against the MGES. An antibody fragment can be a formof an antibody other than the full-length form and includes portions orcomponents that exist within full-length antibodies, in addition toantibody fragments that have been engineered. Antibody fragments caninclude, but are not limited to, single chain Fv (scFv), diabodies, Fv,and (Fab′)₂, triabodies, Fc, Fab, CDR1, CDR2, CDR3, combinations ofCDR's, variable regions, tetrabodies, bifunctional hybrid antibodies,framework regions, constant regions, and the like (see, Maynard et al.,(2000) Ann. Rev. Biomed. Eng. 2:339-76; Hudson (1998) Curr. Opin.Biotechnol. 9:395-402; each herein incorporated by reference in itsentirety). Antibodies can be obtained commercially, custom generated, orsynthesized against an antigen of interest according to methodsestablished in the art (e.g., see Beck et al., Nat Rev Immunol. 2010May; 10(5):345-52; Chan et al., Nat Rev Immunol. 2010 May; 10(5):301-16;and Kontermann, Curr Opin Mol. Ther. 2010 April; 12(2):176-83, each ofwhich are incorporated by reference in their entireties).

Inhibition of RNA encoding a MGES molecule can effectively modulate theexpression of the MGES gene from which the RNA is transcribed.Inhibitors are selected from the group comprising: siRNA, interferingRNA or RNAi; dsRNA; RNA Polymerase III transcribed DNAs; shRNAs;ribozymes; and antisense nucleic acid, which can be RNA, DNA, orartificial nucleic acid.

Antisense oligonucleotides, including antisense DNA, RNA, and DNA/RNAmolecules, act to directly block the translation of mRNA by binding totargeted mRNA and preventing protein translation. For example, antisenseoligonucleotides of at least about 15 bases and complementary to uniqueregions of the DNA sequence encoding a MGES polypeptide can besynthesized, e.g., by conventional phosphodiester techniques (Dallas etal., (2006) Med. Sci. Monit. 12(4):RA67-74; Kalota et al., (2006) Handb.Exp. Pharmacol. 173:173-96; Lutzelburger et al., (2006) Handb. Exp.Pharmacol. 173:243-59; each herein incorporated by reference in itsentirety).

siRNA comprises a double stranded structure containing from about 15 toabout 50 base pairs, for example from about 21 to about 25 base pairs,and having a nucleotide sequence identical or nearly identical to anexpressed target gene or RNA within the cell. Antisense nucleotidesequences include, but are not limited to: morpholinos, 2′-O-methylpolynucleotides, DNA, RNA and the like. RNA polymerase III transcribedDNAs contain promoters, such as the U6 promoter. These DNAs can betranscribed to produce small hairpin RNAs in the cell that can functionas siRNA or linear RNAs that can function as antisense RNA. The MGESmodulating compound can contain ribonucleotides, deoxyribonucleotides,synthetic nucleotides, or any suitable combination such that the targetRNA and/or gene is inhibited. In addition, these forms of nucleic acidcan be single, double, triple, or quadruple stranded. See for exampleBass (2001) Nature, 411, 428 429; Elbashir et al., (2001) Nature, 411,494 498; and PCT Publication Nos. WO 00/44895, WO 01/36646, WO 99/32619,WO 00/01846, WO 01/29058, WO 99/07409, WO 00/44914; each of which areherein incorporated by reference in its entirety.

siRNA can be produced chemically or biologically, or can be expressedfrom a recombinant plasmid or viral vector (for example, see U.S. Pat.No. 7,294,504; U.S. Pat. No. 7,148,342; and U.S. Pat. No. 7,422,896; theentire disclosures of which are herein incorporated by reference).Exemplary methods for producing and testing dsRNA or siRNA molecules aredescribed in U.S. Patent Application Publication No. 2002/0173478 toGewirtz, and in U.S. Patent Application Publication No. 2007/0072204 toHannon et al., the entire disclosures of which are herein incorporatedby reference.

A MGES modulating compound can additionally be a short hairpin RNA(shRNA). The hairpin RNAs can be synthesized exogenously or can beformed by transcribing from RNA polymerase III promoters in vivo.Examples of making and using such hairpin RNAs for gene silencing inmammalian cells are described in, for example, Paddison et al., 2002,Genes Dev, 16:948-58; McCaffrey et al., 2002, Nature, 418:38-9; McManuset al., 2002, RNA, 8:842-50; Yu et al., 2002, Proc Natl Acad Sci USA,99:6047-52; each herein incorporated by reference in its entirety. Suchhairpin RNAs are engineered in cells or in an animal to ensurecontinuous and stable suppression of a desired gene. It is known in theart that siRNAs can be produced by processing a hairpin RNA in the cell.

When a nucleic acid such as RNA or DNA is used that encodes a protein orpeptide of the invention, it can be delivered into a cell in any of avariety of forms, including as naked plasmid or other DNA, formulated inliposomes, in an expression vector, which includes a viral vector(including RNA viruses and DNA viruses, including adenovirus,lentivirus, alphavirus, and adeno-associated virus), by biocompatiblegels, via a pressure injection apparatus such as the Powderject™ systemusing RNA or DNA, or by any other convenient means. Again, the amount ofnucleic acid needed to sequester an Id protein in the cytoplasm can bereadily determined by those of skill in the art, which also can varywith the delivery formulation and mode and whether the nucleic acid isDNA or RNA. For example, see Manjunath et al., (2009) Adv Drug DelivRev. 61(9):732-45; Singer and Verma, (2008) Curr Gene Ther. 8(6):483-8;and Lundberg et al., (2008) Curr Gene Ther. 8(6):461-73; each hereinincorporated by reference in its entirety.

A MGES modulating compound can also be a small molecule that binds tothe MGES and disrupts its function, or conversely, enhances itsfunction. Small molecules are a diverse group of synthetic and naturalsubstances having low molecular weights. They can be isolated fromnatural sources (for example, plants, fungi, microbes and the like), areobtained commercially and/or available as libraries or collections, orsynthesized. Candidate small molecules that modulate MGES can beidentified via in silico screening or high-throughput (HTP) screening ofcombinatorial libraries. Most conventional pharmaceuticals, such asaspirin, penicillin, and many chemotherapeutics, are small molecules,can be obtained commercially, can be chemically synthesized, or can beobtained from random or combinatorial libraries as described herein(Werner et al., (2006) Brief Funct. Genomic Proteomic 5(1):32-6; hereinincorporated by reference in its entirety).

In some embodiments, the compound is selected from the group consistingof etoposide, 5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the compound is selected from the group consistingof 5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the compound is selected from the group consistingof Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.

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and pharmaceutically acceptable salts thereof.

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In some embodiments, the compound is etoposide, 5-fluorouracil, orClostridium difficile Toxin B. In some embodiments, the compound isetoposide. In some embodiments, the compound is 5-fluorouracil. In someembodiments, the compound is Clostridium difficile Toxin B.

Test compounds, such as MGES modulating compounds, can be screened fromlarge libraries of synthetic or natural compounds (see Wang et al.,(2007) Curr Med Chem, 14(2):133-55; Mannhold (2006) Curr Top Med Chem, 6(10):1031-47; and Hensen (2006) Curr Med Chem 13(4):361-76; each hereinincorporated by reference in its entirety). Various methods arecurrently used for random and directed synthesis of saccharide, peptide,and nucleic acid based compounds. Synthetic compound libraries arecommercially available from Maybridge Chemical Co. (Trevillet, Cornwall,UK), AMRI (Albany, N.Y.), ChemBridge (San Diego, Calif.), andMicroSource (Gaylordsville, Conn.). A rare chemical library is availablefrom Aldrich (Milwaukee, Wis.). Alternatively, libraries of naturalcompounds in the form of bacterial, fungal, plant and animal extractsare available from e.g. Pan Laboratories (Bothell, Wash.) or MycoSearch(N.C.), or are readily producible. Additionally, natural andsynthetically produced libraries and compounds are readily modifiedthrough conventional chemical, physical, and biochemical means(Blondelle et al., (1996) Tib Tech 14:60; herein incorporated byreference in its entirety). Many of these compounds are available fromcommercial source vendors such as, for example, Asinex, IBS, ChemBridge,Enamine, Life, TimTech, and Sigma-Aldrich.

Methods for preparing libraries of molecules are well known in the artand many libraries are commercially available. Libraries of interest inthe invention include peptide libraries, randomized oligonucleotidelibraries, synthetic organic combinatorial libraries, and the like.Degenerate peptide libraries can be readily prepared in solution, inimmobilized form as bacterial flagella peptide display libraries or asphage display libraries. Peptide ligands can be selected fromcombinatorial libraries of peptides containing at least one amino acid.Libraries can be synthesized of peptoids and non-peptide syntheticmoieties. Such libraries can further be synthesized which containnon-peptide synthetic moieties, which are less subject to enzymaticdegradation compared to their naturally-occurring counterparts.Libraries are also meant to include for example but are not limited topeptide-on-plasmid libraries, polysome libraries, aptamer libraries,synthetic peptide libraries, synthetic small molecule libraries,neurotransmitter libraries, and chemical libraries. The libraries canalso comprise cyclic carbon or heterocyclic structure and/or aromatic orpolyaromatic structures substituted with one or more of the functionalgroups described herein.

Small molecule combinatorial libraries can also be generated andscreened. A combinatorial library of small organic compounds is acollection of closely related analogs that differ from each other in oneor more points of diversity and are synthesized by organic techniquesusing multi-step processes. Combinatorial libraries include a vastnumber of small organic compounds. One type of combinatorial library isprepared by means of parallel synthesis methods to produce a compoundarray. A compound array can be a collection of compounds identifiable bytheir spatial addresses in Cartesian coordinates and arranged such thateach compound has a common molecular core and one or more variablestructural diversity elements. The compounds in such a compound arrayare produced in parallel in separate reaction vessels, with eachcompound identified and tracked by its spatial address. Examples ofparallel synthesis mixtures and parallel synthesis methods are providedin U.S. Ser. No. 08/177,497, filed Jan. 5, 1994 and its correspondingPCT published patent application WO95/18972, published Jul. 13, 1995 andU.S. Pat. No. 5,712,171 granted Jan. 27, 1998 and its corresponding PCTpublished patent application WO96/22529, each hereby incorporated byreference in its entirety.

Examples of chemically synthesized libraries are described in Fodor etal., (1991) Science 251:767-773; Houghten et al., (1991) Nature354:84-86; Lam et al., (1991) Nature 354:82-84; Medynski, (1994)BioTechnology 12:709-710; Gallop et al., (1994) J. Medicinal Chemistry37(9):1233-1251; Ohlmeyer et al., (1993) Proc. Natl. Acad. Sci. USA90:10922-10926; Erb et al., (1994) Proc. Natl. Acad. Sci. USA91:11422-11426; Houghten et al., (1992) Biotechniques 13:412;Jayawickreme et al., (1994) Proc. Natl. Acad. Sci. USA 91:1614-1618;Salmon et al., (1993) Proc. Natl. Acad. Sci. USA 90:11708-11712; PCTPublication No. WO 93/20242, dated Oct. 14, 1993; and Brenner et al.,(1992) Proc. Natl. Acad. Sci. USA 89:5381-5383. Examples of phagedisplay libraries are described in Scott et al., (1990) Science249:386-390; Devlin et al., (1990) Science, 249:404-406; Christian, etal., (1992) J. Mol. Biol. 227:711-718; Lenstra, (1992) J. Immunol. Meth.152:149-157; Kay et al., (1993) Gene 128:59-65; and PCT Publication No.WO 94/18318. In vitro translation-based libraries include but are notlimited to those described in PCT Publication No. WO 91/05058; andMattheakis et al., (1994) Proc. Natl. Acad. Sci. USA 91:9022-9026. Eachof the foregoing publications are incorporated by reference herein intheir entireties.

Computer modeling and searching technologies permit the identificationof compounds, or the improvement of already identified compounds, thatcan modulate MGES expression or activity. Having identified such acompound or composition, the active sites or regions of a MGES moleculecan be subsequently identified via examining the sites as to which thecompounds bind. These active sites can be ligand binding sites and canbe identified using methods known in the art including, for example,from the amino acid sequences of peptides, from the nucleotide sequencesof nucleic acids, or from study of complexes of the relevant compound orcomposition with its natural ligand. In the latter case, chemical orX-ray crystallographic methods can be used to find the active site byfinding where on the factor the complexed ligand is found.

Screening the libraries can be accomplished by any variety of commonlyknown methods. See, for example, the following references, whichdisclose screening of peptide libraries: Parmley and Smith, (1989) Adv.Exp. Med. Biol. 251:215-218; Scott and Smith, (1990) Science249:386-390; Fowlkes et al., (1992) BioTechniques 13:422-427; Oldenburget al., (1992) Proc. Natl. Acad. Sci. USA 89:5393-5397; Yu et al.,(1994) Cell 76:933-945; Staudt et al., (1988) Science 241:571-580; Bocket al., (1992) Nature 355:564-566; Tuerk et al., (1992) Proc. Natl.Acad. Sci. USA 89:6988-6992; Ellington et al., (1992) Nature355:850-852; U.S. Pat. Nos. 5,096,815; 5,223,409; and 5,198,346, all toLadner et al.; Rebar et al., (1993) Science 263:671-673; and PCT Pub. WO94/18318. Each of the foregoing publications are incorporated byreference herein in their entireties.

The three dimensional geometric structure of an active site, for examplethat of a MGES polypeptide can be determined by known methods in theart, such as X-ray crystallography, which can determine a completemolecular structure. Solid or liquid phase NMR can be used to determinecertain intramolecular distances. Any other experimental method ofstructure determination can be used to obtain partial or completegeometric structures. The geometric structures can be measured with acomplexed ligand, natural or artificial, which can increase the accuracyof the active site structure determined. Potential MGES modulatingcompounds can also be identified using the X-ray coordinates of anotherMGES transcription factor that is similar in structure to a MGES (suchas, Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238). In someembodiments, a compound that binds to a P2RY5 protein can be identifiedvia: (1) providing an electronic library of test compounds; (2)providing atomic coordinates for at least 20 amino acid residues for thebinding pocket of a MGES protein, wherein the coordinates have a rootmean square deviation therefrom, with respect to at least 50% of Caatoms, of not greater than about 5 Å, in a computer readable format; (3)converting the atomic coordinates into electrical signals readable by acomputer processor to generate a three dimensional model of therhodopsin protein, which is similar to the MGES protein; (4) performinga data processing method, wherein electronic test compounds from thelibrary are superimposed upon the three dimensional model of theprotein; and determining which test compound fits into the bindingpocket of the three dimensional model, thereby identifying whichcompound binds to a MGES (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, ZNF238).

Methods for predicting the effect on protein conformation of a change inprotein sequence, are known in the art, and the skilled artisan can thusdesign a variant which functions as an antagonist according to knownmethods. One example of such a method is described by Dahiyat and Mayoin Science (1997) 278:82 87; herein incorporated by reference in itsentirety, which describes the design of proteins de novo. The method canbe applied to a known protein to vary only a portion of the polypeptidesequence. Similarly, Blake (U.S. Pat. No. 5,565,325; herein incorporatedby reference iri its entirety.) teaches the use of known ligandstructures to predict and synthesize variants with similar or modifiedfunction.

Other methods for preparing or identifying peptides that bind to atarget are known in the art. Molecular imprinting, for instance, can beused for the de novo construction of macromolecular structures such aspeptides that bind to a molecule. See, for example, Kenneth J. Shea,Molecular Imprinting of Synthetic Network Polymers: The De Novosynthesis of Macromolecular Binding and Catalytic Sites, TRIP Vol. 2,No. 5, May 1994; Mosbach, (1994) Trends in Biochem. Sci., 19(9); andWulff, G., in Polymeric Reagents and Catalysts (Ford, W. T., Ed.) ACSSymposium Series No. 308, pp 186-230, American Chemical Society (1986);each herein incorporated by reference in its entirety. One method forpreparing mimics of a MGES modulating compound involves the steps of:(i) polymerization of functional monomers around a known substrate (thetemplate) that exhibits a desired activity; (ii) removal of the templatemolecule; and then (iii) polymerization of a second class of monomersin, the void left by the template, to provide a new molecule whichexhibits one or more desired properties which are similar to that of thetemplate. In addition to preparing peptides in this manner other bindingmolecules such as polysaccharides, nucleosides, drugs, nucleoproteins,lipoproteins, carbohydrates, glycoproteins, steroids, lipids, and otherbiologically active materials can also be prepared. This method isuseful for designing a wide variety of biological mimics that are morestable than their natural counterparts, because they are prepared by thefree radical polymerization of functional monomers, resulting in acompound with a nonbiodegradable backbone. Other methods for designingsuch molecules include for example drug design based on structureactivity relationships, which require the synthesis and evaluation of anumber of compounds and molecular modeling.

MGES modulating compounds of the invention can be incorporated intopharmaceutical compositions suitable for administration, for example incombination with a pharmaceutically acceptable carrier. The compositionscan be administered alone or in combination with at least one otheragent, such as a stabilizing compound, which can be administered in anysterile, biocompatible pharmaceutical carrier including, but not limitedto, saline, buffered saline, dextrose, and water. The compositions canbe administered to a patient alone, or in combination with other agents,drugs or hormones.

In some embodiments, the composition comprises a compound selected fromthe group consisting of etoposide, 5-fluorouracil, Clostridium difficileToxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the composition comprises a compound selected fromthe group consisting of 5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the composition comprises a compound selected fromthe group consisting of Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.

In some embodiments, the composition comprises a compound selected fromthe group consisting of

and pharmaceutically acceptable salts thereof.

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In some embodiments, the composition comprises etoposide,5-fluorouracil, or Clostridium difficile Toxin B. In some embodiments,the composition comprises etoposide. In some embodiments, thecomposition comprises 5-fluorouracil. In some embodiments, thecomposition comprises Clostridium difficile Toxin B.

Pharmaceutical Compositions and Administration Therapy

According to the invention, a pharmaceutically acceptable carrier cancomprise any and all solvents, dispersion media, coatings, antibacterialand antifungal agents, isotonic and absorption delaying agents, and thelike, compatible with pharmaceutical administration. The use of suchmedia and agents for pharmaceutically active substances is well known inthe art. Any conventional media or agent that is compatible with theactive compound can be used. Supplementary active compounds can also beincorporated into the compositions.

An MGES protein (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, and ZNF238) or an MGES modulating compound can be administeredto the subject one time (e.g., as a single injection or deposition).Alternatively, and MGES protein or compounds of the invention can beadministered once or twice daily to a subject in need thereof for aperiod of from about 2 to about 28 days, or from about 7 to about 10days, or from about 7 to about 15 days. It can also be administered onceor twice daily to a subject for a period of 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12 times per year, or a combination thereof. Furthermore, anMGES protein or a MGES modulating compound can be co-administrated withanother therapeutic, such as a chemotherapy drug.

Some non-limiting examples of conventional chemotherapy drugs include:aminoglutethimide, amsacrine, asparaginase, bcg, anastrozole, bleomycin,buserelin, bicalutamide, busulfan, capecitabine, carboplatin,camptothecin, chlorambucil, cisplatin, carmustine, cladribine,colchicine, cyclophosphamide, cytarabine, dacarbazine, cyproterone,clodronate, daunorubicin, diethylstilbestrol, docetaxel, dactinomycin,doxorubicin, dienestrol, etoposide, exemestane, filgrastim,fluorouracil, fludarabine, fludrocortisone, epirubicin, estradiol,gemcitabine, genistein, estramustine, fluoxymesterone, flutamide,goserelin, leuprolide, hydroxyurea, idarubicin, levamisole, imatinib,lomustine, ifosfamide, megestrol, melphalan, interferon, irinotecan,letrozole, leucovorin, ironotecan, mitoxantrone, nilutamide,medroxyprogesterone, mechlorethamine, mercaptopurine, mitotane,nocodazole, octreotide, methotrexate, mitomycin, paclitaxel,oxaliplatin, temozolomide, pentostatin, plicamycin, suramin, tamoxifen,porfimer, mesna, pamidronate, streptozocin, teniposide, procarbazine,titanocene dichloride, raltitrexed, rituximab, testosterone,thioguanine, vincristine, vindesine, thiotepa, topotecan, tretinoin,vinblastine, trastuzumab, and vinorelbine.

In some embodiments, the chemotherapy drug is an alkylating agent, anitrosourea, an anti-metabolite, a topoisomerase inhibitor, a mitoticinhibitor, an anthracycline, a corticosteroid hormone, a sex hormone, ora targeted anti-tumor compound.

A targeted anti-tumor compound is a drug designed to attack cancer cellsmore specifically than standard chemotherapy drugs can. Most of thesecompounds attack cells that harbor mutations of certain genes, or cellsthat overexpress copies of these genes. In some embodiments, theanti-tumor compound can be imatinib (Gleevec), gefitinib (Iressa),erlotinib (Tarceva), rituximab (Rituxan), or bevacizumab (Avastin).

An alkylating agent works directly on DNA to prevent the cancer cellfrom propagating. These agents are not specific to any particular phaseof the cell cycle. In some embodiments, alkylating agents can beselected from busulfan, cisplatin, carboplatin, chlorambucil,cyclophosphamide, ifosfamide, dacarbazine (DTIC), mechlorethamine(nitrogen mustard), melphalan, and temozolomide.

An antimetabolite makes up the class of drugs that interfere with DNAand RNA synthesis. These agents work during the S phase of the cellcycle and are commonly used to treat leukemias, tumors of the breast,ovary, and the gastrointestinal tract, as well as other cancers. In someembodiments, an antimetabolite can be 5-fluorouracil, capecitabine,6-mercaptopurine, methotrexate, gemcitabine, cytarabine (ara-C),fludarabine, or pemetrexed.

Topoisomerase inhibitors are drugs that interfere with the topoisomeraseenzymes that are important in DNA replication. Some examples oftopoisomerase I inhibitors include topotecan and irinotecan while somerepresentative examples of topoisomerase II inhibitors include etoposide(VP-16) and teniposide.

Anthracyclines are chemotherapy drugs that also interfere with enzymesinvolved in DNA replication. These agents work in all phases of the cellcycle and thus, are widely used as a treatment for a variety of cancers.In some embodiments, an anthracycline used with respect to the inventioncan be daunorubicin, doxorubicin (Adriamycin), epirubicin, idarubicin,or mitoxantrone.

An MGES protein or an MGES modulating compound of the invention can beadministered to a subject by any means suitable for delivering theprotein or compound to cells of the subject. For example, it can beadministered by methods suitable to transfect cells. Transfectionmethods for eukaryotic cells are well known in the art, and includedirect injection of the nucleic acid into the nucleus or pronucleus of acell; electroporation; liposome transfer or transfer mediated bylipophilic materials; receptor mediated nucleic acid delivery,bioballistic or particle acceleration; calcium phosphate precipitation,and transfection mediated by viral vectors.

The compositions of this invention can be formulated and administered toreduce the symptoms associated with a nervous system cancer (e.g, aglioma) by any means that produce contact of the active ingredient withthe agent's site of action in the body of a human or non-human subject.They can be administered by any conventional means available for use inconjunction with pharmaceuticals, either as individual therapeuticactive ingredients or in a combination of therapeutic activeingredients. They can be administered alone, but are generallyadministered with a pharmaceutical carrier selected on the basis of thechosen route of administration and standard pharmaceutical practice.

Pharmaceutical compositions for use in accordance with the invention canbe formulated in conventional manner using one or more physiologicallyacceptable carriers or excipients. The therapeutic compositions of theinvention can be formulated for a variety of routes of administration,including systemic and topical or localized administration. Techniquesand formulations generally can be found in Remmington's PharmaceuticalSciences, Meade Publishing Co., Easton, Pa. (20^(1h) ed., 2000), theentire disclosure of which is herein incorporated by reference. Forsystemic administration, an injection is useful, includingintramuscular, intravenous, intraperitoneal, and subcutaneous. Forinjection, the therapeutic compositions of the invention can beformulated in liquid solutions, for example in physiologicallycompatible buffers, such as PBS, Hank's solution, or Ringer's solution.In addition, the therapeutic compositions can be formulated in solidform and redissolved or suspended immediately prior to use. Lyophilizedforms are also included. Pharmaceutical compositions of the presentinvention are characterized as being at least sterile and pyrogen-free.These pharmaceutical formulations include formulations for human andveterinary use.

Any of the therapeutic applications described herein can be applied toany subject in need of such therapy, including, for example, a mammalsuch as a dog, a cat, a cow, a horse, a rabbit, a monkey, a pig, asheep, a goat, or a human. Thus, in some embodiments, the subject is amammal. In some embodiments, the subject is a dog, a cat, a cow, ahorse, a rabbit, a monkey, a pig, a sheep, a goat, or a human. In someembodiments, the subject is a dog, a monkey, or a human. In someembodiments, the subject is a human.

A pharmaceutical composition of the invention is formulated to becompatible with its intended route of administration. Examples of routesof administration include parenteral, e.g., intravenous, intradermal,subcutaneous, oral (e.g., inhalation), transdermal (topical),transmucosal, and rectal administration. Solutions or suspensions usedfor parenteral, intradermal, or subcutaneous application can include thefollowing components: a sterile diluent such as water for injection,saline solution, fixed oils, polyethylene glycols, glycerine, propyleneglycol or other synthetic solvents; antibacterial agents such as benzylalcohol or methyl parabens; antioxidants such as ascorbic acid or sodiumbisulfite; chelating agents such as ethylenediaminetetraacetic acid;buffers such as acetates, citrates or phosphates and agents for theadjustment of tonicity such as sodium chloride or dextrose. pH can beadjusted with acids or bases, such as hydrochloric acid or sodiumhydroxide. The parenteral preparation can be enclosed in ampoules,disposable syringes or multiple dose vials made of glass or plastic.

Pharmaceutical compositions suitable for injectable use include sterileaqueous solutions (where water soluble) or dispersions and sterilepowders for the extemporaneous preparation of sterile injectablesolutions or dispersions. For intravenous administration, suitablecarriers include physiological saline, bacteriostatic water, CremophorEM™ (BASF, Parsippany, N.J.) or phosphate buffered saline (PBS). Thecomposition must be sterile and fluid to the extent that easysyringability exists. It must be stable under the conditions ofmanufacture and storage and must be preserved against the contaminatingaction of microorganisms such as bacteria and fungi. The carrier can bea solvent or dispersion medium containing, for example, water, ethanol,a pharmaceutically acceptable polyol like glycerol, propylene glycol,liquid polyetheylene glycol, and suitable mixtures thereof. The properfluidity can be maintained, for example, by the use of a coating such aslecithin, by the maintenance of the required particle size in the caseof dispersion and by the use of surfactants. Prevention of the action ofmicroorganisms can be achieved by various antibacterial and antifungalagents, for example, parabens, chlorobutanol, phenol, ascorbic acid, andthimerosal. In many cases, it can be useful to include isotonic agents,for example, sugars, polyalcohols such as mannitol, sorbitol, sodiumchloride in the composition. Prolonged absorption of the injectablecompositions can be brought about by including in the composition anagent which delays absorption, for example, aluminum monostearate andgelatin.

Sterile injectable solutions can be prepared by incorporating the MGESmodulating compound in the required amount in an appropriate solventwith one or a combination of ingredients enumerated herein, as required,followed by filtered sterilization. Dispersions are prepared byincorporating the active compound into a sterile vehicle which containsa basic dispersion medium and the required other ingredients from thoseenumerated herein. In the case of sterile powders for the preparation ofsterile injectable solutions, examples of useful preparation methods arevacuum drying and freeze-drying which yields a powder of the activeingredient plus any additional desired ingredient from a previouslysterile-filtered solution thereof.

Oral compositions include an inert diluent or an edible carrier. Theycan be enclosed in gelatin capsules or compressed into tablets. For thepurpose of oral therapeutic administration, the active compound can beincorporated with excipients and used in the form of tablets, troches,or capsules. Oral compositions can also be prepared using a fluidcarrier for use as a mouthwash, wherein the compound in the fluidcarrier is applied orally and swished and expectorated or swallowed.

Pharmaceutically compatible binding agents, and/or adjuvant materialscan be included as part of the composition. The tablets, pills,capsules, troches and the like can contain any of the followingingredients, or compounds of a similar nature: a binder such asmicrocrystalline cellulose, gum tragacanth or gelatin; an excipient suchas starch or lactose, a disintegrating agent such as alginic acid,Primogel, or corn starch; a lubricant such as magnesium stearate orsterotes; a glidant such as colloidal silicon dioxide; a sweeteningagent such as sucrose or saccharin; or a flavoring agent such aspeppermint, methyl salicylate, or orange flavoring.

Systemic administration can also be by transmucosal or transdermalmeans. For transmucosal or transdermal administration, penetrantsappropriate to the barrier to be permeated are used in the formulation.Such penetrants are known in the art, and include, for example, fortransmucosal administration, detergents, bile salts, and fusidic acidderivatives. Transmucosal administration can be accomplished through theuse of nasal sprays or suppositories. For transdermal administration,the active compounds are formulated into ointments, salves, gels, orcreams as known in the art

A composition of the invention can be administered to a subject in needthereof. Subjects in need thereof can include but are not limited to,for example, a mammal such as a dog, a cat, a cow, a horse, a rabbit, amonkey, a pig, a sheep, a goat, or a human. Thus, in some embodiments,the subject is a mammal. In some embodiments, the subject is a dog, acat, a cow, a horse, a rabbit, a monkey, a pig, a sheep, a goat, or ahuman. In some embodiments, the subject is a dog, a monkey, or a human.In some embodiments, the subject is a human.

A composition of the invention can also be formulated as a sustainedand/or timed release formulation. Such sustained and/or timed releaseformulations can be made by sustained release means or delivery devicesthat are well known to those of ordinary skill in the art, such as thosedescribed in U.S. Pat. Nos. 3,845,770; 3,916,899; 3,536,809; 3,598,123;4,008,719; 4,710,384; 5,674,533; 5,059,595; 5,591,767; 5,120,548;5,073,543; 5,639,476; 5,354,556; and 5,733,566, the entire disclosuresof which are each incorporated herein by reference. The pharmaceuticalcompositions of the invention (e.g, that have a therapeutic effect) canbe used to provide slow or sustained release of one or more of theactive ingredients using, for example, hydropropylmethyl cellulose,other polymer matrices, gels, permeable membranes, osmotic systems,multilayer coatings, microparticles, liposomes, microspheres, or thelike, or a combination thereof to provide the desired release profile invarying proportions. Suitable sustained release formulations known tothose of ordinary skill in the art, including those described herein,can be readily selected for use with the pharmaceutical compositions ofthe invention. Single unit dosage forms suitable for oraladministration, such as, but not limited to, tablets, capsules,gel-caps, caplets, or powders, that are adapted for sustained releaseare encompassed by the invention.

In the methods described herein, an MGES protein or a MGES modulatingcompound can be administered to the subject either as RNA, inconjunction with a delivery reagent, or as a nucleic acid (e.g., arecombinant plasmid or viral vector) comprising sequences which expressthe gene product. Suitable delivery reagents for administration of theMGES protein or compounds include the Mirus Transit TKO lipophilicreagent; lipofectin; lipofectamine; cellfectin; or polycations (e.g.,polylysine), or liposomes.

The dosage administered can be a therapeutically effective amount of thecomposition sufficient to result in amelioration of symptoms of anervous system cancer in a subject (e.g, a decrease or inhibition ofnervous system tumor cell proliferation, a decrease or inhibition ofangiogenesis), and can vary depending upon known factors such as thepharmacodynamic characteristics of the active ingredient and its modeand route of administration; time of administration of activeingredient; age, sex, health and weight of the recipient; nature andextent of symptoms; kind of concurrent treatment, frequency of treatmentand the effect desired; and rate of excretion.

In some embodiments, the effective amount of the administered MGESpolypetide, MGES, polynucleotide, or MGES modulating compound is atleast about 0.01 μg/kg body weight, at least about 0.025 μg/kg bodyweight, at least about 0.05 μg/kg body weight, at least about 0.075μg/kg body weight, at least about 0.1 μg/kg body weight, at least about0.25 μg/kg body weight, at least about 0.5 μg/kg body weight, at leastabout 0.75 μg/kg body weight, at least about 1 μg/kg body weight, atleast about 5 μg/kg body weight, at least about 10 μg/kg body weight, atleast about 25 μg/kg body weight, at least about 50 μg/kg body weight,at least about 75 μg/kg body weight, at least about 100 μg/kg bodyweight, at least about 150 μg/kg body weight, at least about 200 μg/kgbody weight, at least about 250 μg/kg body weight, at least about 300μg/kg body weight, at least about 350 μg/kg body weight, at least about400 μg/kg body weight, at least about 450 μg/kg body weight, at leastabout 500 μg/kg body weight, at least about 550 μg/kg body weight, atleast about 600 μg/kg body weight, at least about 650 μg/kg body weight,at least about 700 μg/kg body weight, at least about 750 μg/kg bodyweight, at least about 800 μg/kg body weight, at least about 850 μg/kgbody weight, at least about 900 μg/kg body weight, at least about 950μg/kg body weight, or at least about 1000 μg/kg body weight.

In some embodiments, the effective amount of the administered MGESpolypetide, MGES, polynucleotide, or MGES modulating compound is atleast about 0.1 mg/kg body weight, at least about 0.3 mg/kg body weight,at least about 0.5 mg/kg body weight, at least about 0.75 mg/kg bodyweight, at least about 1 mg/kg body weight, at least about 5 mg/kg bodyweight, at least about 10 mg/kg body weight, at least about 25 mg/kgbody weight, at least about 50 mg/kg body weight, at least about 75mg/kg body weight, at least about 100 mg/kg body weight, at least about150 mg/kg body weight, at least about 200 mg/kg body weight, at leastabout 250 mg/kg body weight, at least about 300 mg/kg body weight, atleast about 350 mg/kg body weight, at least about 400 mg/kg body weight,at least about 450 mg/kg body weight, at least about 500 mg/kg bodyweight, at least about 550 mg/kg body weight, at least about 600 mg/kgbody weight, at least about 650 mg/kg body weight, at least about 700mg/kg body weight, at least about 750 mg/kg body weight, at least about800 mg/kg body weight, at least about 850 mg/kg body weight, at leastabout 900 mg/kg body weight, at least about 950 mg/kg body weight, or atleast about 1000 mg/kg body weight.

In some embodiments, an MGES protein or a MGES modulating compound isadministered at least once daily. In some embodiments, an MGES proteinor a MGES modulating compound is administered at least twice daily. Insome embodiments, an MGES protein or a MGES modulating compound isadministered for at least 1 week, for at least 2 weeks, for at least 3weeks, for at least 4 weeks, for at least 5 weeks, for at least 6 weeks,for at least 8 weeks, for at least 10 weeks, for at least 12 weeks, forat least 18 weeks, for at least 24 weeks, for at least 36 weeks, for atleast 48 weeks, or for at least 60 weeks. In some embodiments, an MGESprotein and/or an MGES modulating compound is administered incombination with a second therapeutic agent.

Toxicity and therapeutic efficacy of therapeutic compositions of thepresent invention can be determined by standard pharmaceuticalprocedures in cell cultures or experimental animals, e.g., fordetermining the LD₅₀ (the dose lethal to 50% of the population) and theED₅₀ (the dose therapeutically effective in 50% of the population). Thedose ratio between toxic and therapeutic effects is the therapeuticindex and it can be expressed as the ratio LD₅₀/ED₅₀. Therapeutic agentsthat exhibit large therapeutic indices are useful. Therapeuticcompositions that exhibit some toxic side effects can be used.

Gene Therapy and Protein Replacement Methods

In one aspect, the invention provides methods for treating a nervoussystem cancer in a subject, e.g., a glioma. In some embodiments, themethod can comprise administering to the subject an MGES molecule (e.g,a MGES polypeptide or a MGES polynucleotide) or a MGES modulatingcompound, which can be a polypeptide, small molecule, antibody, or anucleic acid.

Various approaches can be carried out to restore the activity orfunction of an MGES gene (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1,FosL2, bHLH-B2, or ZNF238) in a subject, such as those carrying analtered MGES gene locus. For example, supplying wild-type MGES genefunction (such as, e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2,or ZNF238) to such subjects can suppress the phenotype of a nervoussystem cancer in a subject (e.g., nervous system tumor cellproliferation, mervous system tumor size, or angiogenesis). Increasingand/or decreasing MGES gene expression levels or activity (such as,e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, or ZNF238) can beaccomplished through gene or protein therapy.

A nucleic acid encoding an MGES gene, or a functional part thereof canbe introduced into the cells of a subject. For example, the wild-typegene (or a functional part thereof) can also be introduced into thecells of the subject in need thereof using a vector as described herein.The vector can be a viral vector or a plasmid. The gene can also beintroduced as naked DNA. The gene can be provided so as to integrateinto the genome of the recipient host cells, or to remainextra-chromosomal. Integration can occur randomly or at preciselydefined sites, such as through homologous recombination. For example, afunctional copy of an MGES gene can be inserted in replacement of analtered version in a cell, through homologous recombination. Furthertechniques include gene gun, liposome-mediated transfection, or cationiclipid-mediated transfection. Gene therapy can be accomplished by directgene injection, or by administering ex vivo prepared geneticallymodified cells expressing a functional polypeptide.

Delivery of nucleic acids into viable cells can be effected ex vivo, insitu, or in vivo by use of vectors, and more particularly viral vectors(e.g., lentivirus, adenovirus, adeno-associated virus, or a retrovirus),or ex vivo by use of physical DNA transfer methods (e.g., liposomes orchemical treatments). Non-limiting techniques suitable for the transferof nucleic acid into mammalian cells in vitro include the use ofliposomes, electroporation, microinjection, cell fusion, DEAE-dextran,and the calcium phosphate precipitation method (see, for example,Anderson, Nature, supplement to vol. 392, no. 6679, pp. 25-20 (1998);herein incorporated by reference in its entirety). Introduction of anucleic acid or a gene encoding a polypeptide of the invention can alsobe accomplished with extrachromosomal substrates (transient expression)or artificial chromosomes (stable expression). Cells may also becultured ex vivo in the presence of therapeutic compositions of thepresent invention in order to proliferate or to produce a desired effecton or activity in such cells. Treated cells can then be introduced invivo for therapeutic purposes.

Nucleic acids can be inserted into vectors and used as gene therapyvectors. A number of viruses have been used as gene transfer vectors,including papovaviruses, e.g., SV40 (Madzak et al., 1992; hereinincorporated by reference in its entirety), adenovirus (Berkner, 1992;Berkner et al., 1988; Gorziglia and Kapikian, 1992; Quantin et al.,1992; Rosenfeld et al., 1992; Wilkinson et al., 1992;Stratford-Perricaudet et al., 1990; each herein incorporated byreference in its entirety), vaccinia virus (Moss, 1992; hereinincorporated by reference in its entirety), adeno-associated virus(Muzyczka, 1992; Ohi et al., 1990; each herein incorporated by referencein its entirety), herpesviruses including HSV and EBV (Margolskee, 1992;Johnson et al., 1992; Fink et al., 1992; Breakfield and Geller, 1987;Freese et al., 1990; each herein incorporated by reference in itsentirety), and retroviruses of avian (Biandyopadhyay and Temin, 1984;Petropoulos et al., 1992; each herein incorporated by reference in itsentirety), murine (Miller, 1992; Miller et al., 1985; Sorge et al.,1984; Mann and Baltimore, 1985; Miller et al., 1988; each hereinincorporated by reference in its entirety), and human origin (Shimada etal., 1991; Helseth et al., 1990; Page et al., 1990; Buchschacher andPanganiban, 1992; each herein incorporated by reference in itsentirety). Non-limiting examples of in vivo gene transfer techniquesinclude transfection with viral (typically retroviral) vectors (see U.S.Pat. No. 5,252,479; herein incorporated by reference in its entirety)and viral coat protein-liposome mediated transfection (Dzau et al.,Trends in Biotechnology 11:205-210 (1993); herein incorporated byreference in its entirety). For example, naked DNA vaccines aregenerally known in the art; see Brower, Nature Biotechnology,16:1304-1305 (1998); herein incorporated by reference in its entirety.Gene therapy vectors can be delivered to a subject by, for example,intravenous injection, local administration (see, e.g., U.S. Pat. No.5,328,470; herein incorporated by reference in its entirety) or bystereotactic injection (see, e.g., Chen, et al., 1994. Proc. Natl. Acad.Sci. USA 91: 3054-3057; herein incorporated by reference in itsentirety). The pharmaceutical preparation of the gene therapy vector caninclude the gene therapy vector in an acceptable diluent, or cancomprise a slow release matrix in which the gene delivery vehicle isimbedded. Alternatively, where the complete gene delivery vector can beproduced intact from recombinant cells, e.g., retroviral vectors, thepharmaceutical preparation can include one or more cells that producethe gene delivery system.

For reviews of gene therapy protocols and methods see Anderson et al.,Science 256:808-813 (1992); U.S. Pat. Nos. 5,252,479, 5,747,469,6,017,524, 6,143,290, 6,410,010 6,511,847; and U.S. ApplicationPublication Nos. 2002/0077313 and 2002/00069; each herein incorporatedby reference in its entirety. For additional reviews of gene therapytechnology, see Friedmann, Science, 244:1275-1281 (1989); Verma,Scientific American: 68-84 (1990); Miller, Nature, 357: 455-460 (1992);Kikuchi et al., J Dermatol Sci. 2008 May; 50(2):87-98; Isaka et al.,Expert Opin Drug Deliv. 2007 September; 4(5):561-71; Jager et al., CurrGene Ther. 2007 August; 7(4):272-83; Waehler et al., Nat Rev Genet. 2007August; 8(8):573-87; Jensen et al., Ann Med. 2007; 39(2):108-15;Herweijer et al., Gene Ther. 2007 January; 14(2):99-107; Eliyahu et al.,Molecules, 2005 Jan. 31; 10(1):34-64; and Altaras et al., Adv BiochemEng Biotechnol. 2005; 99:193-260; each herein incorporated by referencein its entirety.

Protein replacement therapy can increase the amount of protein byexogenously introducing wild-type or biologically functional protein byway of infusion. A replacement polypeptide can be synthesized accordingto known chemical techniques or may be produced and purified via knownmolecular biological techniques. Protein replacement therapy has beendeveloped for various disorders. For example, a wild-type protein can bepurified from a recombinant cellular expression system (e.g., mammaliancells or insect cells-see U.S. Pat. No. 5,580,757 to Desnick et al.;U.S. Pat. Nos. 6,395,884 and 6,458,574 to Selden et al.; U.S. Pat. No.6,461,609 to Calhoun et al.; U.S. Pat. No. 6,210,666 to Miyamura et al.;U.S. Pat. No. 6,083,725 to Selden et al.; U.S. Pat. No. 6,451,600 toRasmussen et al.; U.S. Pat. No. 5,236,838 to Rasmussen et al. and U.S.Pat. No. 5,879,680 to Ginns et al.; each herein incorporated byreference in its entirety), human placenta, or animal milk (see U.S.Pat. No. 6,188,045 to Reuser et al.; herein incorporated by reference inits entirety), or other sources known in the art. After the infusion,the exogenous protein can be taken up by tissues through non-specific orreceptor-mediated mechanism.

These methods described herein are by no means all-inclusive, andfurther methods to suit the specific application is understood by theordinary skilled artisan. Moreover, the effective amount of thecompositions can be further approximated through analogy to compoundsknown to exert the desired effect.

Nervous System Tumors and Tumor Targets

In some embodiments, the invention can be used to treat various nervoussystem tumors, for example gliomas (e.g., astrocytomas (such asanaplastic astrocytoma), Glioblastoma Multiforme (GBM),oligodendrogliomas, ependymoma) and meningiomas. The nervous systemtumor can include, but is not limited to, cerebellar astrocytoma,medulloblastoma, ependymona, brain stem glioma, optic nerve glioma,acoustic neuromas, nerve sheath tumors, or germinoma. In someembodiments, the methods for treating cancer relate to methods forinhibiting proliferation of a cancer or tumor cell comprisingadministering to a subject a protein or other agent that decreasesexpression of a MGES gene (e.g., Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2,bHLH-B2, ZNF238, or a combination thereof) of the tumor or cancer cell.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Exemplary methods and materialsare described below, although methods and materials similar orequivalent to those described herein can also be used in the practice ortesting of the present invention.

All publications and other references mentioned herein are incorporatedby reference in their entirety, as if each individual publication orreference were specifically and individually indicated to beincorporated by reference. Publications and references cited herein arenot admitted to be prior art.

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, many equivalents to the specificembodiments of the invention described herein. Such equivalents areintended to be within the scope of the present invention.

The invention is further described by the following non-limitingExamples.

EXAMPLES

Examples are provided herein to facilitate a more complete understandingof the invention. The following examples illustrate the exemplary modesof making and practicing the invention. However, the scope of theinvention is not limited to specific embodiments disclosed in theseExamples, which are for purposes of illustration only, since alternativemethods can be utilized to obtain similar results.

Example 1 Id Proteins Stimulate Axonal Elongation

Recent work from the laboratory identified a new and unexpected functionfor Id proteins, namely the ability to stimulate axonal elongation(Iavarone and Lasorella, 2006; Lasorella et al., 2006; each hereinincorporated by reference in its entirety). These studies originatedfrom the identification of the Anaphase Promoting Complex (APC) as theubiquitin ligase that primes Id2 for proteasomal-mediated degradation.Degradation of Id2 by APC is mediated by a highly conserved sequence,the destruction box (D-box), which is required for recognition by theAPC co-activator Cdhl. It was found that mutation of the D-box of Id2(Id2-DBM) resulted in marked stabilization of the protein in neuralcells. Previous work had shown that APC-Cdhl restrains axonal growth indifferent types of CNS neurons (Konishi et al., 2004; hereinincorporated by reference in its entirety). However, the natural targetsof APC-Cdhl for the axonal growth phenotype remained unknown. The recentstudies identified those targets. It was found that introduction ofId2-DBM in cortical and cerebellar neurons was sufficient to enhanceaxonal growth and override the inhibitory effects on axonal elongationimposed by myelin components. These effects are implemented byId2-mediated silencing of a gene expression response induced by bHLHtranscription factors. The products of the bHLHinducible genes repressedby Id2 in neurons are secreted molecules (Sema3F), ligands (jagged-2)and receptors (Nogo Recepotor, Unc5A, Notch-I) of multiple inhibitoryand repellant signals for axons (Barallobre et al., 2005; Fiore andPuschel, 2003; Lesuisse and Martin, 2002; Sestan et al., 1999; Spenceret al., 2003; each herein incorporated by reference in its entirety).

Recovery following spinal cord injury (SCI) is limited because severedaxons of the CNS fail to regenerate. Neverthless, some recovery ofsensory and motor functions occurs over the first few weeks followingincomplete injuries. Without being bound by theory, the most importantMechanism responsible for this recovery is the trigger of injury-inducedplasticity, a phenomenon manifested by the establishment of newintraspinal circuits in the lesioned area. Although the mechanismspromoting injury-induced plasticity are poorly understood, an importantevent is up-regulation of genes that stimulate axonal growth andneurotrophic factors (jun, NT-3, BDNF, etc.) (Becker and Bonni, 2005;herein incorporated by reference in its entirety). Remarkably, injury ofmany types of neurons in vivo is associated with upregulation of Idgenes. Without being bound by theory, expression of Id2 can generatebeneficial effects for regeneration of damaged axons in the CNS.

Experimental Plan and Methods:

Here, the results observed in vitro following introduction ofundegradable Id2 into neurons are extended to a mouse model of spinalcord injury. To do this, a pilot study will be performed usingadeno-associated viruses (AAV) encoding Id2-DBM in mice that havereceived a spinal cord injury. Without being bound by theory, micetransduced with AAV-Id2-DBM will regenerate axons more efficiently thancontrol mice (infected with AAVGFP) and display greater functionallocomotor recovery.

Delivery of Virus.

The delivery system to be used is injection of the sensory-motor cortexwith the AAV-based constructs. AAV is the most effective system tointroduce exogenous proteins in post-mitotic neurons in the adult animal(Kaspar et al., 2003; Xiao et al., 1997; each herein incorporated byreference in its entirety). The most striking aspect of AAV transductionin the CNS is the absence of expression of the exogenous gene in glialcells (Burger et al., 2004; Passini et al., 2006; each hereinincorporated by reference in its entirety). The AAV5 serotype wasselected based on its superior ability to transducer mammalian brain incomparison with the other AAV serotypes (Passini et al., 2006; hereinincorporated by reference in its entirety).

AAV5-Id2-DBM and AAV5-GFP will be produced and purified by Virapur (SanDiego, Calif.) by cotransfection of Helper plasmid and a plasmidexpressing the AAV5 rep and cap genes. To evaluate whether introductionof Id2-DBM promotes axonal regeneration in the CST, 5 μl of each viralpreparation (approximate titer: 2×10¹¹ genome copies/ml) will bestereotactically injected into the motor cortex of 20 mice (10 withAAV-GFP, 10 with AAV-Id2-DBM) using a single needle tract. In anadditional group of 20 mice the AAVs will be injected directly in thespinal cord to transduce propriospinal neurons and evaluate whetherId2-DBM stimulates formation of new circuits and leads to betterfunctional recovery in the behavioral tests. The total of 40 mice willundergo lateral hemisection injury of the thoracic spinal cord withsevering of the dorsal cortico-spinal tract (CST) in the dorsalfuniculus as well as the lateral CST. During the same operation as thelesion procedure, animals will be randomly divided into the twoexperimental groups (20 mice injected with AAVGFP, 20 mice injected withAAV-Id2-DBM) and will undergo stereotactic injection with each virus inthe sensory-motor cortex controlateral to the lesion site or will bedirectly injected in the lesioned area of the spinal cord. The studywill be terminated three months after SCI/AAV injection when the animalswill be analyzed with end-point behavioral tests and sacrificed forpathological examination. Surgical and behavioral procedures will beperformed at the CRF SCI Core, after which perfused, collected tissuewill be shipped for histological analysis.

Behavioral Testing.

Animals will be monitored to analyze behavioral recovery weekly for nineweeks after injury in an open field environment by the BBB.Quantification will be performed in a blinded manner by two observers.Three months after lesion and just before sacrificing, the animals willbe videotaped on a horizontal ladder beam test in a series of threetrials and scored over 150 rungs by two independent observers. They willalso undergo a final stage kinematic locomotor testing using CatWalk andDigiGait analysis. Results will be analyzed for statisticallysignificant differences between the two experimental groups either atwo-way ANOVA or by using a paired t test (significance <0.05).

Pathological Examination.

The integrity of the dorsal CST will be assessed by tracer(biotindextran amine, BDA) injection into the bilateral sensory-motorcortices 14 to 21 days prior to sacrifice. The retrograde tracerFluorogold will also be injected below the injury site. Blocks extending5 mm rostral and 5 mm caudal to the center of the injury will besectioned in the sagittal plane. The far-rostral as well as thefar-caudal segments will be sectioned in the transverse plane. Thespinal cord will be dissected, fixed, embedded and sectioned. On eachsection the number of intersections of BDA-labeled fibers with adorso-ventral line will be counted from 4 mm above to 4 mm below thelesion site. Axon number will be calculated as a percentage of thefibers seen 4 cm above the lesion where the CST is intact. Forimmunohistochemistry, frozen tissue will be obtained from an uninjuredspinal cord and from each animal group.

REFERENCES

-   Barallobre, M. J., Pascual, M., Del Rio, J. A., and Soriano, E.    (2005). The Netrin family of guidance factors: emphasis on Netrin-1    signalling. Brain Res Brain Res Rev 49, 22-47.-   Becker, E. B., and Bonni, A. (2005). Beyond proliferation—cell cycle    control of neuronal survival and differentiation in the developing    mammalian brain. Semin Cell Dev Biol 16, 439-448.-   Burger, C., Gorbatyuk, O, S., Velardo, M. J., Peden, C. S.,    Williams, P., Zolotukhin, S., Reier, P. J., Mandel, R. J., and    Muzyczka, N. (2004). Recombinant AAV viral vectors pseudotyped with    viral capsids from serotypes 1, 2, and 5 display differential    efficiency and cell tropism after delivery to different regions of    the central nervous system. Mol Ther 10, 302-317.-   Fiore, R., and Puschel, A. W. (2003). The function of semaphorins    during nervous system development. Front Biosci 8, s484-499.-   Iavarone, A., and Lasorella, A. (2004). Id proteins in neural    cancer. Cancer Lett 204, 189-196.-   Iavarone, A., and Lasorella, A. (2006). ID proteins as targets in    cancer and tools in neurobiology. Trends Mol Med 12, 588-594.-   Kaspar, B. K., Llado, J., Sherkat, N., Rothstein, J. D., and    Gage, F. H. (2003). Retrograde viral delivery of IGF-1 prolongs    survival in a mouse ALS model. Science 301, 839-842.-   Konishi, Y., Stegmuller, J., Matsuda, T., Bonni, S., and Bonni, A.    (2004). Cdhl-APC controls axonal growth and patterning in the    mammalian brain. Science 303, 1026-1030.-   Lasorella, A., Stegmuller, J., Guardavaccaro, D., Liu, G., Carro, M.    S., Rothschild, G., de la Torre-Ubieta, L., Pagano, M., Bonni, A.,    and Iavarone, A. (2006). Degradation of Id2 by the    anaphase-promoting complex couples cell cycle exit and axonal    growth. Nature 442, 471-474.-   Lasorella, A., Uo, T., and Iavarone, A. (2001). Id proteins at the    cross-road of development and cancer. Oncogene 20, 8326-8333.-   Lesuisse, C., and Martin, L. J. (2002). Long-term culture of mouse    cortical neurons as a model for neuronal development, aging, and    death. J Neurobiol 51, 9-23.-   Norton, J. D., Deed, R. W., Craggs, G., and Sablitzky, F. (1998). Id    helix-loop-helix proteins in cell growth and differentiation. Trends    Cell Biol 8, 58-65.-   Passini, M. A., Dodge, J. C., Bu, J., Yang, W., Zhao, Q., Sondhi,    D., Hackett, N. R., Kaminsky, S. M., Mao, Q., Shihabuddin, L. S., et    al. (2006). Intracranial delivery of CLN2 reduces brain pathology in    a mouse model of classical late infantile neuronal ceroid    lipofuscinosis. J Neurosci 26, 1334-1342.-   Perk, J., Iavarone, A., and Benezra, R. (2005). Id family of    helix-loop-helix proteins in cancer. Nat Rev Cancer 5, 603-614.-   Sestan, N., Artavanis-Tsakonas, S., and Rakic, P. (1999).    Contact-dependent inhibition of cortical neurite growth mediated by    notch signaling. Science 286, 741-746.-   Spencer, T., Domeniconi, M., Cao, Z., and Filbin, M. T. (2003). New    roles for old proteins in adult CNS axonal regeneration. Curr Opin    Neurobiol 13, 133-139.-   Xiao, X., Li, J., McCown, T. J., and Samulski, R. J. (1997). Gene    transfer by adeno-associated virus vectors into the central nervous    system. Exp Neurol 144, 113-124.-   Ying, Q. L., Nichols, J., Chambers, I., and Smith, A. (2003). BMP    induction of Id proteins suppresses differentiation and sustains    embryonic stem cell self-renewal in collaboration with STAT3. Cell    115, 281-292.

Example 2 Transcriptional Regulation Module in High-Grade Glioma

Computational Identification of the MGES Transcriptional RegulationModule in High-Grade Glioma.

To identify Master Transcriptional Modules (MTM) and MRs of the MGES,ARACNe was applied to 176 AA and GBM samples (22, 66, 77; each hereinincorporated by reference in its entirety), which had been previouslyclassified into three molecular signature groups—proneural,proliferative, and mesenchymal (MGES) —by unsupervised cluster analysis(77; herein incorporated by reference in its entirety). The MasterRegulator Analysis (MRA) algorithm was developed to infer acomprehensive repertoire of candidate MRs, regulating 102 genes thatwere overexpressed in the MGES. First, TFs were identified by theirannotation in the Gene Ontology (3; herein incorporated by reference inits entirety). Then, for each TF the Fisher Exact Test (FET) was used todetermine whether the intersection of its ARACNe predicted targets (theTF-regulon) with the MGES genes was statistically significant. From aglobal list of 1018 TFs, the MRA produced a subset of 55 MGES-specific,candidate MRs, at a False Discovery Rate, FDR ≦0.05. Among the 55candidate MRs in the ARACNe network, the top six (Stat3, C/EBPβ/δ,bHLH-B2, Runx1, FosL2, and ZNF238) appear to collectively regulate 74%of the MGES genes (FIG. 1). This is a lower bound because ARACNe has alow false positive rate but a higher false negative rate. Falsenegatives are not an issue in this analysis, as long as the number ofTF-targets in the regulon is sufficient to assess statisticallysignificant enrichment of MGES genes.

Multiple dataset and modality integration, using machine learningapproaches such as Naïve Bayes classifiers, has been shown tosignificantly outperform individual analyses (36; herein incorporated byreference in its entirety). Additionally, since ARACNe trades off a lowfalse-positive rate for a higher false-negative rate, appropriateintegration of ARACNe's inferences from multiple datasets will beespecially useful to achieve higher coverage of transcriptionalinteractions. Convergence of ARACNe inferences from distinct datasetswas successfully shown (49; herein incorporated by reference in itsentirety). High overlap between Master Regulators inferred from ARACNeanalysis of completely independent Breast Cancer datasets wasdemonstrated. Thus, integration of target predictions from multipledatasets can improve the algorithm's performance without requiring dataconsolidation into a single dataset, which invariably introducesartifacts due to dataset specific bias.

Consistent with their previously reported activity, Pearson correlationanalysis shows that five of the top six MRs (Stat3, C/EBPβ/δ, bHLH-B2,Runx1, and FosL2) are mostly activators of their regulon genes and onlyone (ZNF238) is a suppressor (2, 23; each herein incorporated byreference in its entirety). This can further indicate their respectivepotential as oncogenes or tumor suppressors. Since both C/EBPβ andC/EBPδ were among the top inferred MRs and they are known to formstoichiometric homo and heterodimers, with identical DNA bindingspecificity and redundant transcriptional activity (79; hereinincorporated by reference in its entirety), the term C/EBP genericallywill be used to indicate these transcriptional complexes. The FETp-values for the enrichment of the MGES genes in the ARACNe-inferred MRregulons are respectively: ρ_(FosL2)=3.5E 44, ρ_(ZNF238)=3.1E 31,ρ_(bHLH-B)2=3.0E 29, ρ_(Runx1)=7.8E 24, ρ_(Stat3)=1.2E 21,ρ_(C/EBPβ)=3.2E 15. Thus, candidate MR regulons are highly enriched inMGES genes. The regulons of the six TFs show highly significant overlap,indicating their potential role in the combinatorial regulation of theMGES. Since TFs' expression is correlated, FET cannot computestatistical significance of this overlap. Significance was thus computedby comparing regulon overlap of each MR-pair against that of randomTF-pairs with equivalent Mutual Information. Table I shows number ofshared targets (lower left triangle) and p-value of regulon overlap(upper right triangle). For the TF pairs, the intersection between theirregulon and the MGES is highly significant. This'further supports therole of these genes in a combinatorial Master Regulator Module (MRM),which controls the MGES program of GBM.

TABLE 1 Intersect between TFs and ARACNe targets (mesenchymal genes).The number of mesenchymal genes shared as first neighbor by each pair ofTFs is reported on the lower left of the table. The statisticalsignificance of the target overlap for each pair of TFs after correctionfor the correlation of the pair is shown on the upper right side of thetable. The reported P-values are test of independence between two TFs'neighborhoods considering Mutual Information between TFs' geneexpression profiles. TF BHLHB2 CEBPB FOSL2 RUNX1 STAT3 ZNF238 BHLHB2 300.0e+00 0.0e+00 1.7e−03 2.5e−02 5.7e−03 CEBPB 12 20 0.0e+00 0.0e+005.2e−03 0.0e+00 FOSL2 23 18 48 0.0e+00 0.0e+00 0.0e+00 RUNX1 16 16 29 420.0e+00 0.0e+00 STAT3 10  9 20 21 30 0.0e+00 ZNF238 13 14 27 26 25 39

Number of MES Targets

Alternative and Complementary MRA Analysis Tools.

Stepwise Linear Regression (SLR) was used to construct quantitative,albeit simplified MGES transcriptional regulation models (i.e.regulatory programs). In such models, log-expression of MGES genes iscomputed as a linear function of the log-expression of a few TFs (14,96; each herein incorporated by reference in its entirety).Specifically, log 2 expression of the i-th MGES gene is the responsevariable and the log 2 expression of the TFs are the explanatoryvariables in the linear model log 2 xi=Σα_(ij) log₂ f_(j)+β_(ij) (96).Here, f_(j) represents the expression of the j-th TF in the model andthe (α_(ij), β_(ij)) are linear coefficients computed by standardregression analysis. TFs were iteratively added to the model, bychoosing the one yielding the smallest relative errorE=Σ|x_(i)−x_(i0)|/x_(i0) between predicted and observed targetexpression. This was repeated until the decrease in relative error wasno longer statistically significant, thus effectively preventingoverfitting. TFs were chosen only among the 55 MRA-inferred MRs and TFswhose DNA binding signature was highly enriched in the proximal promoterof MGES genes and with a coefficient of variation (CV≧0.5), indicating areasonable expression range in the dataset. This significantly reducesthe number of candidate TFs. TFs were ranked based on the number of MGEStarget programs they affected. Surprisingly, the top six MRA-inferredTFs were among the top eight SLR-inferred TFs, showing significantrobustness and consistency of the methods. The three TFs with thehighest average coupling coefficients ( α _(i)=Σ_(i)α_(ij)) were C/EBP(α_(i)=0.42), bHLH-B2 (α_(i)=0.41), and Stat3 (α_(i)=0.40), furtherindicating their potential role as MRs, with the next strongestmodulator, ZNF238, showing a negative coefficient (α_(i)=−0.34)indicating its role as a transcriptional repressor.

Analysis of Candidate MRs in Human Glioma.

To analyze the expression patterns of the six candidate MRs,semi-quantitative RT-PCR was used in an independent set of 17 primarymalignant gliomas. The analysis included both normal human brain and theglioma cell line SNB75 whose expression profile correlates with themesenchymal centroid. bHLH-B2, C/EBPβ, FosL2, Stat3 and Runx1 wereexpressed in the SNB75 cell line. Expression of each of these TFs waspresent and concordant in at least 9 of 17 tumor samples (FIG. 2). Thisis in agreement with the reported incidence of malignant glioma with amesenchymal phenotype (−50%) (77; herein incorporated by reference inits entirety). The Runx1 transcript was almost uniform in tumor samplesand was also detectable in normal brain. Importantly, bHLH-B2, C/EBPβand FosL2 transcripts were absent in normal brain, thus indicating apossible specific role of these TFs in gliomagenesis and/or progression.Stat3 levels were higher in GBM samples carrying high expression ofbHLH-B2, C/EBPβ and FosL2. Conversely, expression of ZNF238 was readilydetectable in normal brain but absent in SNB75 cells and in primarygliomas with the exception of one sample (#2) that displayed minimalexpression levels (FIG. 2). This finding is consistent with the notionthat the ability of ZNF238 to function as repressor of the MGES confersto the ZNF238 gene a tumor suppressor activity that is invariablyabrogated in malignant glioma.

Biochemical Validation of MR Binding Sites.

Each candidate MR was tested for its ability to bind to the promoterregion (proximal regulatory DNA) of its predicted MGES targets. Thetarget promoters were first analyzed in silico to identify putativebinding sites. Promoter analysis was performed using the MatInspectorsoftware (www.genomatix.de; herein incorporated by reference in itsentirety). A sequence of 2 kb upstream and 2 kb downstream from thetranscription start site was analyzed for the presence of putativebinding sites for each MR. ChIP assays were then performed near the bestpredicted site for each MR-target in the human glioma cell line SNB75,to validate targets of Stat3, bHLH-B2, C/EBPβ and FosL2, for whichappropriate reagents were available. On average, 80% of the testedgenomic regions can be immunoprecipitated by MR-specific antibodies butnot by control antibodies (FIG. 3). Since binding can be co-factormediated or occur in other promoter regions, this constitutes alower-bound on the percent of MR-bound MGES genes. One can conclude thatARACNe accurately recapitulates the transcriptional activity of Stat3,bHLH-B2, C/EBPβ and FosL2 on the MGES genes in malignant gliomas.

Candidate MRs Form a Highly Connected and Hierarchically OrganizedMaster Regulator Module.

From recent results in yeast and mammalian cells, MRs of key cellularprocesses (a) are involved in auto-regulatory (AR), feedback (FB), andfeed-forward (FF) loops (44, 68; each herein incorporated by referencein its entirety), (b) participate in highly interconnected TF modules(12; herein incorporated by reference in its entirety), and (c) areorganized within hierarchical control structures (108; hereinincorporated by reference in its entirety). Thus, whether the topologyof the five candidate MRs involved in positive control of the MGESdisplayed such properties was considered. ChIP assays revealed thatStat3 and C/EBP occupy their own promoter and are thus involved in ARloops (FIGS. 4A-B). Additionally, Stat3 occupies the FosL2 and Runx1promoters; C/EBPβ occupies those of Stat3, FosL2, bHLH-B2, C/EBPβ, andC/EBPδ (the latter two confirm the redundant autoregulatory activity ofthe two C/EBP subunits, FIG. 4B) (65, 79; each herein incorporated byreference in its entirety); FosL2 occupies those of Runx1 and bHLH-B2(FIG. 4C); finally bHLH-B2 occupies only that of Runx1 (FIG. 4D). Theregulatory topology emerging from promoter occupancy analysis is thushighly interconnected (12/15 possible interactions are implemented), hasa hierarchical structure and is very rich in FF loops (FIG. 4E). Thelarge number of FF loops can contribute to lower the MGES programsensitivity to short, random fluctuations (37; herein incorporated byreference in its entirety). Stat3 and C/EBP, which are also involved inAR and FF loops with a large fraction of MGES genes, appear to be at thetop of the hierarchy. Lentivirus-mediated shRNA silencing of Stat3 andC/EBPβ in human GBM-derived stem-like cells (GBM-BTSCs) led todownregulation of the other TFs, confirming the hierarchical MRMorganization (FIG. 4F). Without being bound by theory, (a) at least fiveof the six MRs participate in a hierarchical MRM control structure and(b) Stat3 and C/EBP can be master initiators and regulators of themesenchymal signature of malignant gliomas.

Combined Expression of C/EBPβ and Stat3 Prevents NeuronalDifferentiation and Induces Mesenchymal and Oncogenic Transformation ofNSCs.

Without being bound by theory, NSCs are the cell of origin for malignantgliomas in the mesenchymal subgroup (77; herein incorporated byreference in its entirety). However, whether mesenchymal transformationin glial tumors recapitulates a normal albeit rare cell fatedetermination event intrinsic to NSCs remains unknown (95, 98, 105; eachherein incorporated by reference in its entirety). Whether combinedexpression of Stat3 and C/EBPβ in NSCs is sufficient to initiatemesenchymal gene expression and to trigger the mesenchymal propertiesthat characterize high-grade glioma was considered. An early passage ofthe stable, clonal population of mouse NSCs known as C17.2 was usedbecause its enhanced yet constitutively self-regulated expression ofsternness genes permits its cells to be efficiently grown asundifferentiated monolayers in sufficiently large, homogeneous andviable quantities to ensure reproducible patterns of self-renewal anddifferentiation without ever behaving in a tumorigenic fashion in vitroor in vivo (43, 72, 74; each herein incorporated by reference in itsentirety). Following ectopic expression of C/EBPβ and a constitutivelyactive form of Stat3 (Stat3C, 13; herein incorporated by reference inits entirety), dramatic morphologic changes of NSCs were observed,consistent with loss of ability to differentiate along the neuronallineage (FIG. 5A). Parental and vector-transfected NSCs have theclassical spindle-shaped morphology that is associated with the neuralstem/progenitor cell phenotype. When grown in the absence of mitogens,these cells display efficient neuronal differentiation characterized byformation of a neuritic network (FIG. 5A, top-right panel). Conversely,expression of C/EBPβ and Stat3C leads to cellular flattening andmanifestation of a fibroblast-like morphology. Remarkably, depletion ofmitogens resulted in additional flattening with complete loss of everyneuronal trait (FIG. 5A, bottom-right panel). These results indicatethat expression of C/EBPβ and Stat3C efficiently suppressesdifferentiation along the neuronal lineage and induces mesenchymalfeatures.

Next, whether C/EBPβ and Stat3C induce expression of the respectivetargets predicted by ARACNe and, more importantly, whether the inducedexpression pattern is consistent with that of the global MGES wasconsidered. mRNA was extracted from duplicate samples of two independentC/EBPβ/Stat3C expressing and control clones of NSCs and hybridizedcustom expression arrays (Agilent Technologies) containing probes for6,308 glioma-specific mouse and human genes. The Gene Set EnrichmentAnalysis method (GSEA) (92; herein incorporated by reference in itsentirety) was used to test the enrichment of the mesenchymal,proliferative and proneural signatures (77; herein incorporated byreference in its entirety) among differentially expressed genes inC/EBPβ/Stat3C-expressing versus control cells. In this method, theKolmogorov-Smirnoff test is used to determine whether two gene lists arestatistically correlated. In this case, one list includes genes on themicroarray expression profile dataset, ranked by their differentialexpression statistics across two conditions (e.g. ectopically expressedStat3C-C/EBPβ vs. control), from most over- to most under-expressed. Theother list contains non-ranked genes in a specific signature (e.g.mesenchymal). This is very useful to detect, for instance, situationswhere signature genes can be differentially expressed as a whole, eventhough the fold-change can be small for each gene in isolation. In thiscase, a gene-by-gene test, such as a T-test, can not be able to revealstatistical significance. The algorithm was set to implement weightedscoring scheme and the enrichment score significance is assessed by1,000 permutation tests to compute the enrichment p-value. The analysisdemonstrated that the global mesenchymal and proliferative signaturesare both highly enriched in genes that are overexpressed inC/EBPβ/Stat3C-expressing NSCs. Conversely, the proneural signature isenriched in genes that are underexpressed in these cells (FIG. 5B). Asubset of Stat3 and C/EBPβ targets of the microarray results wasvalidated by quantitative RT-PCR (qRT-PCR).

Next, whether activation of the MGES by Stat3 and C/EBPβ is sufficientto transform NSCs into cells that can efficiently migrate and invade wasconsidered. Two assays were used to address this question. The first(“wound assay”) evaluates the ability to migrate and fill a scratchintroduced in cultures of adherent cells (FIG. 5C). The second(“Matrigel invasion assay”) tests how cells invade a Boyden chamberfilter coated with a physiologic mixture of extracellular matrixcomponents and concentrate the lower side of the filter (FIG. 5D). Whenthe two assays were performed on C/EBPβ/Stat3C-expressing and controlNSCs clones, it was found that the expression of the two TFs robustlypromoted migration and invasion through the extracellular matrix (FIGS.5C-D). The effects of C/EBPβ and Stat3C on migration and invasion ofNSCs were similar in the absence of mitogens or in the presence of PDGF(FIG. 5D). Similarly, ectopic bHLH-B2 was irrelevant for the MGES andphenotypic behavior of Stat3C-C/EBPβ-expressing NSCs.

To ask whether Stat3 and C/EBPβ confer tumorigenic potential to NSCs invivo, sub-cutaneous heterotopic transplantation of C17.2-Stat3C-C/EBPβ(or empty vector as control) was used. C17.2-Stat3C/C/EBPβ cellsdeveloped fast-growing tumors with high efficiency (4 out of 4 mice inthe group injected with 5×10⁶ cells and 3 out of 4 mice in the groupinjected with 2.5×10⁶ cells), whereas NSCs transduced with empty vectornever formed tumors (FIG. 6A). Histological analysis demonstrated thatthe tumors resembled human high grade glioma, exhibited large areas ofpolymorphic cells, had tendency to form pseudopalisades with centralnecrosis and although injected in the flank, a low angiogenic site,displayed extensive vascular proliferation, as confirmed byimmunostaining for the endothelial marker CD31 (FIGS. 6B-C).Proliferation in the tumors was very high as determined by reactivityfor Ki67. In line with the presence of stem-like cells, human GBMregularly exhibit expression of primitive markers. Corroborating this,it was found that the tumors stained positive for the progenitor markernestin (FIG. 6C). Finally, positive immunostaining for the mesenchymalsignature proteins OSMR and the FGF receptor-1 (FGFR-1) indicated thatoncogenic transformation of neural stem cells had occurred in thecontext of reprogramming towards the mesenchymal lineage (FIG. 6D).Together, these findings establish that introduction of the two MRs ofMGES in NSCs not only induces expression of the entire MGES but is alsosufficient to transduce to these cells the key phenotypiccharacteristics of glioma aggressiveness that have been previouslyassociated with that signature.

Stat3 and C/EBPβ are Essential for Expression of the MGES andAggressiveness of Human Glioma Cells and Primary Tumors.

To assess the significance of constitutive Stat3 and C/EBPβ in cellsresponsible for glioma tumor growth in humans, it was sought to abolishthe expression of Stat3 and C/EBPβ in GBM-derived brain tumor stem-likecells that closely mimic the biology of the parental primary tumors andretain tumor-initiating capacity (GBM-BTSCs, 42; herein incorporated byreference in its entirety). Transduction of GBM-BTSCs with specificshRNA-carrying lentiviruses efficiently silenced endogenous Stat3 andC/EBPβ (FIG. 7A). Expression analysis using GSEA and qRT-PCR showed thatdepletion of Stat3 and C/EBPβ in GBM-BTSCs dramatically suppressedexpression of the MGES genes (FIGS. 7B-C). Next, the “mesenchymal” humanglioma cell line SNB19 was infected with shStat3 and shC/EBPβlentiviruses and confirmed that silencing of Stat3 and C/EBPβ depletedthe mesenchymal signature even in established glioma cell lines (FIG.7D). Furthermore, silencing of the two TFs in SNB19 eliminated 80% oftheir ability to invade through Matrigel (FIG. 7E).

As final test for the mesenchymal regulatory role of Stat3 and C/EBPβ inhuman glioma, an immunohistochemical analysis for C/EBPβ and active,phospho-Stat3 in human tumor specimens was conducted and compared theexpression of these TFs with YKL-40 (a well-established mesenchymalprotein also known as CHI3L1, Refs. 66, 75; each herein incorporated byreference in its entirety) as well as patient outcome in a collection of62 newly diagnosed GBMs. FET showed that expression of either C/EBPβ andStat3 were significantly correlated with YKL-40 expression (C/EBPβ,p=4.9×10⁻⁵; Stat3, p=2.2×10⁻⁴). However, the correlation was higher whendouble positive tumors (C/EBPβ+/Stat3+) were compared to doublenegatives (C/EBPβ−/Stat3−, p=2.7×10⁻⁶). Furthermore, double positivetumors were associated with markedly worse clinical outcome than tumorsthat were either single or double negatives (log-rank test, p=0.0002,FIG. 7F). Positivity for either of the two TFs remained predictive ofnegative outcome but with lower statistical strength than doublepositivity (C/EBPβ, p=0.0022; Stat3, p=0.0017). These results providecompelling indication that the synergistic activation of C/EBPβ andStat3 generates mesenchymal properties and marks the poorest survival inpatients with GBM.

Computational Inference of MR Modulators.

MINDy is the first algorithm for the systematic identification ofpost-translational modulators of TF activity (100, 101; each hereinincorporated by reference in its entirety). It identifies candidateTF-modulators by testing whether, given the expression of a putativemodulator gene, the Conditional Mutual Information (CMI) I[TF; t|M]between a TF and one of its targets changes as a function of theavailability of M. In Ref. 102 (herein incorporated by reference in itsentirety), four modulators of the MYC TF in human B cells, including theSTK38 kinase, the HDACl histone deacetylase, and two co-TF factors,bHLH-B2 and MEF2B were biochemically validated. FIG. 8 showsexperimental data supporting the role of STK38 as a post-translationalmodulator of MYC activity. Experimental evidence for the other validatedmodulators is provided in the appendix (102; herein incorporated byreference in its entirety). In Ref. 100 (herein incorporated byreference in its entirety), MINDy analysis was extended tosystematically reverse-engineer the interface between ˜800 signalingproteins (including protein kinases, phosphatases, and cell surfacereceptors) and an equivalent number of TFs expressed in human B cells.STK38 was experimentally validated as a pleiotropic serine-threoninekinase, affecting not just MYC but several other TFs. Thus, MINDy isable to identify post-translational modulators of transcriptionalprograms. For details on MINDy implementation, see Refs 55, 100; eachherein incorporated by reference in its entirety.

MINDy's applicability has been significantly enhanced by theavailability of a large set of microarray expression profile for highgrade glioma from The Genome Cancer ATLAS/TCGA effort. This dataset isnow equivalent in statistical power to the human B cell dataset used forthe development of the MINDy approach. As discussed herein, the newMINDy analysis of Stat3 modulators recapitulates the major direct andpathway mediated modulators of Stat3 activity and demonstrates thefeasibility of the MINDy algorithm. In Ref. 55 (herein incorporated byreference in its entirety), it was shown that MINDy outputs were able tobuild a genome-wide interactome and to infer both causal oncogeniclesions as well as mechanism of action of specific chemicalperturbations. Furthermore, in Ref. 100 (herein incorporated byreference in its entirety), the complete and biochemically validatedanalysis of the interface between signaling proteins and TFs in human Bcells was reported. Results from the latter, as also shown in FIG. 8,have allowed the computational identification of kinases silenced bylentivirus-mediated transduction of shRNA constructs in human B cell,using only transcriptional data.

A key requirement of the algorithm is the availability of ≧200 GEPs, sothat the Conditional MI dependency on the modulator can be accuratelymeasured. False negatives further improve with higher sample sizes (i.e.fewer modulators are missed). Studies were limited by a sample size thatwas too small to be effective (176 samples). However, a set of 236GBM-related GEPs was recently made available by the ATLAS/TCGA project(1). Using this larger dataset sufficient statistical power was achievedto infer several post-translational modulators of Stat3 and C/EBPβactivity. MINDy-inferred modulators can be used for two independentgoals. First, preliminary analysis of gene copy number (GCN) alterationsfrom matched TCGA samples revealed that several genes encoding Stat3 andC/EBPβ modulators harbor genetic alterations in high-grade glioma,supporting their potential tumorigenic role (Table 2).

TABLE 2 Summary table of the post-translational modulators of STAT3 andCEBPβ identified by MINDy in two separate analyses. Shown are TF andsignaling modulators, having significant copy number aberrationsenrichment in patients with high expression of YKL40, selected as markergene. Patients were binned into three categories, high, medium and low,according to the YKL40 expression level. Modulators are calledsignificant whenever there is an enrichment in the frequency of patientsfor the corresponding aberration with a p-value of the χ2 < 5% and aresorted left to right by decreasing number of affected targets. SourceAnalysis TF Fsym STAT3 CEBPB Cytoband 7q31.2 10p11.21 17q21 7q32 22q12.219q13.31 10p12 17q21 10p12 Modulator TFEC CREM RARA IRF5 PATZ1 ZNF576MSRB2 RARA MLLT10 YKL40 high expression levels Enrichment in Yes No NoYes No Yes No No No Amplifications Enrichment in No Yes Yes No Yes NoYes Yes Yes Deletions Lowest χ 1.43% 0.46% 4.55% 2.18% 3.39% 2.53% 0.46%4.55% 0.46% Significant Amplifica Deletion Deletion Amplifica DeletionAmplifica Deletion Deletion Deletion Alteration Source AnalysisSignaling Fsym STAT3 CEBPB Cytoband 10q26 7q22-q31.1 19q13.3 7p12 10q2619q13 22q12|7p14.3-p14.1 Modulator FGFR2 SRPK2 PRKD2 EGFR FGFR2 VRK3CAMK2B YKL40 high expression levels Enrichment in No Yes Yes Yes No YesYes Amplifications Enrichment in Yes No No No Yes No No Deletions Lowestχ 4.12% 1.05% 4.55% 0.67% 4.12% 4.55% 0.47% Significant DeletionAmplifica Amplification Amplification Deletion Amplifica AmplificationAlteration

This is important because the Stat3 and C/EBPβ loci are not directtargets of genetic alterations in GBM. Hence, one can predict thatgenetic alterations can target their upstream regulators. Specifically,several GCN alterations of Stat3 and C/EBPβ modulators co-segregate withoverexpression of YKL40, a marker of MGES activation. Without beingbound by theory, genetic alterations of the modulator genes canirreversibly activate these MRs, thus leading to constitutive activationof the MGES in high-grade glioma. Second, the modulator proteins canconstitute appropriate drug targets for therapeutic intervention.

The inferred repertoire of Stat3 modulators was compared to literaturedata (21, 34; each herein incorporated by reference in its entirety).The analysis revealed that several inferred modulators are known toregulate Stat3 activity post-translationally, either by direct physicalinteraction, or by effecting well-characterized pathways known to affectStat3 function, mostly through phsphorylation cascades. Among theputative Stat3 modulators, we found the β2 adrenergic receptor (ADRB2)and Src kinase Lyn, which mediate phosphorylation and activation ofStat3 (103, 107; each herein incorporated by reference in its entirety).Conversely, the cdk2 and GSK3β kinases and the tumor suppressor PTEN arenegative regulators of Stat3 phosphorylation and activity (10, 90, 93;each herein incorporated by reference in its entirety). Our approach wasalso able to identify the α subunit of Protein Kinase C(PRKCA), the MAPkinase MEK2 (MAP2K2) and the Receptor 2 for FGF (FGFR2), three essentialcomponents of signaling pathways known to modulate Stat3 activity (28,39, 71, 73; each herein incorporated by reference in its entirety).Finally, MINDy identified Dyrk2 as a Stat3 modulator and, in screeningassays Dyrk kinases have emerged as phosphorylation kinases for Stat3(60; herein incorporated by reference in its entirety). These findingsmirror those obtained for MYC (101, 102; each herein incorporated byreference in its entirety) and indicate that MINDy is effective in theidentification of post-translational modulators of MR activity.

Conclusions.

Computational, ChIP and functional experiments, motivated by theinferred network topology, showed that Stat3 and C/EBP are key MRs ofthe MGES. However, the participation of the transcriptional repressorZNF238 as a principal negative regulator of the mesenchymal signature,combined with the invariable loss of expression of ZNF238 in primaryGBM, indicate that the full manifestation of the MGES inevitablyrequires elimination of the constraints imposed by ZNF238. Initialresults will be followed up with a comprehensive computationalreconstruction of the transcriptional and post-translationalinteractions that structure the regulatory network driving the MGES. Themechanisms used by glioma cells to silence the expression of ZNF238 willalso be determined and tested whether this TF is a tumor suppressor genein malignant brain tumors. Finally, computational approaches will beused to identify post-translational modulators of the ‘mesenchymal TFs’and validate in vivo their functional activity and their value astherapeutic targets.

Future Directions.

As shown in this Example, use of tumor biopsy GEPs was sufficient todiscover candidate synergistic oncogenes and tumor suppressor genes.However, the highly heterogeneous nature of the disease can preventdissection of many TF-targets and upstream modulators. Given thedecreasing cost of GEP microarrays and the availability ofhigh-throughput robotic platforms available to us, a new, highlyinformative dataset will be assembled using a cellular context that ishighly specific to the transformation under study. Specifically, aconnectivity map (40; herein incorporated by reference in its entirety),using ˜200 chemical perturbations of human GBM-derived BTSCs will beproduced. These cells represent the best cellular model for human GBMbecause they closely mimic the genotype, gene expression profile and invivo biology of their parental primary tumor (42, 99; each hereinincorporated by reference in its entirety).

Furthermore, it was shown that MGES expression in GBM-BTSCs requires theactivity of the MRs Stat3 and C/EBPβ (FIG. 7). Therefore, GBM-BTSCsrepresent a model human cellular system to produce a glioma connectivitymap and to study regulation of the MRs of mesenchymal signature GBM invitro. This new dataset will be highly complementary to the GBM dataproduced by the TCGA project and is of critical importance to achievethe aims of this proposal. Specifically, while TCGA GEPs represent thenatural physiologic variability of GBM samples and can be representativeof a variety of diverse genetic and epigenetic abnormalities, theconnectivity map will reflect the response of high-grade (mesenchymal)glioma to non-physiologic (i.e., chemical) perturbations. Thus, thecombination of the two resources will allow optimal dissection of bothtype of processes.

Compound Selection and Optimization.

˜200 compounds will be prioritized by analysis of MCF7, PC3, HL60, andSK-MEL5 connectivity map data (40; herein incorporated by reference inits entirety). Optimal compounds will be those producing the mostinformative profiles. Several methods can be used for this analysis,including Principal Component Analysis (PCA), unsupervised clustering,and greedy optimization techniques to select maximum-entropy GEPsubsets, among others. The Genome wide 44Kx12 Illumina array (HumanHT-12Expression BeadChip) supports analysis of ˜200 assays (in replicate) andappropriate controls for approximately. As opposed to Ref. 40 (hereinincorporated by reference in its entirety), where compounds werescreened at a fixed 10 μM concentration in DMSO, the selected compoundswill be profiled at multiple concentrations to determine optimalparameters for ˜10% growth inhibition of GBM-BTSCs, G110, after 48 h.This will optimize the screening, providing maximally informative data.Higher concentrations can produce largely equivalent cellular stressresponses (e.g., apoptosis), while lower concentrations will producelittle or no effects on cell dynamics.

Perturbation Assays and Microarray Expression Profiling.

GBM-BTSCs will be treated with selected compounds at G110 concentrationin replicate, harvested after 6 h (to minimize secondary responseeffects), and profiled using the Illumina HumanHT-12 Expression BeadChiparray. These monitor ˜44,000 probes covering known human alternativesplice transcripts. Appropriate negative controls will be generatedusing the compound delivery medium (DMSO). Arrays will be hybridized andread by the Columbia Cancer Center genomic core facility. The lab hassignificant experience using the Illumina array, including automationand optimization of mRNA extraction and labeling protocols on theHamilton Star microfluidic station. Since ARACNe requires >100 GEPs andMINDy requires >250 GEPs to achieve sufficient statistical power, thedataset (˜400 GEPs) is adequately powered to support both analyses. Theresulting dataset will be referred to as the High-grade GliomaConnectivity Map (HGCM). Additionally, two public datasets will beanalyzed including expression profiles from tumor samples (42, 77; eachherein incorporated by reference in its entirety) as well as the 236samples from the TCGA, identified respectively as HGEP_(Lee), HGEP_(Ph),and HGEP_(TCGA).

Example 3 Creation of a High-Accuracy Map of Regulatory InteractionsEffecting the MGES of High-Grade Glioma

In this example, the molecular interaction networks and transcriptionalmodules that regulate the mesenchymal phenotype of malignant glioma willbe dissected, modeled, and interrogated. This phenotype, which displaysa specific genetic signature identified by molecular profiling, ischaracterized by the activation of several genes involved ininvasiveness and tumor angiogenesis and has been associated with a verypoor prognosis. Genes causally involved in tumorigenesis or responsiblefor the aggressiveness of the malignant phenotype will be identified.Furthermore, computational tools will be designed and used to integratethe rich source of genetic, epigenetic, and functional data assembled byThe Genome Cancer ATLAS/TCGA project on Glioblastoma Multiforme (GBM) toidentify “druggable” proteins that can affect the mesenchymal phenotype,thus providing appropriate targets for therapeutic intervention (seeEXAMPLES 5-7).

To find the Master Regulators of a malignant phenotype, the ARACNealgorithm, developed for the dissection of mammalian transcriptionalnetworks and validated, will be coupled with new algorithms that modelthe regulatory process, by integrating DNA binding signatures.Preliminary studies (EXAMPLE 2) show that ARACNe identifies a small,tightly connected, self-regulating module comprising six transcriptionfactors (TFs) that appears to regulate the mesenchymal signature ofhuman high-grade glioma. This example discusses the reverse engineeringand dissection of crucial mechanisms involved in the pathogenesis ofGBM, one of the most lethal forms of human cancer.

A reverse engineering computational approach will be applied to dissectand validate the transcriptional network that drives the mesenchymalphenotype of high-grade glioma. The expression of mesenchymal andangiogenesis-associated genes in malignant human glioma is associatedwith very poor clinical outcome. ARACNe (Algorithm for theReconstruction of Accurate Cellular Networks), one of the toolsdeveloped by the Columbia National Center for Biomedical Computing(MAGNet), has been used to identify transcription factors regulating amesenchymal gene expression signature associated with poor prognosis.The latter was identified by hierarchical clustering of a widecollection of microarray expression profiles of malignant glioma.

The analysis has identified a highly interconnected module of sixtranscription factors that regulate each other as well as the vastmajority of the mesenchymal genes. The computational analyses as alsobeen extended to new algorithms able to predict post-translationalmodulators of the master transcriptional regulators (MINDy, ModulatorInference by Network Dynamics). New computational tools will be designedand used to integrate the many sources of genetic, epigenetic andfunctional date available on human brain tumors. The goals are: toreconstruct and experimentally manipulate the transcriptional andpost-translational programs responsible for the expression of themesenchymal signature of high-grade glioma (see EXAMPLE 2 and herein);to elucidate the mechanism by which high-grade glioma silence ZNF238, atranscriptional repressor of the mesenchymal signature, and test therole of ZNF238 gene inactivation in gliomagenesis in the mouse (EXAMPLE4); to computationally identify and experimentally validate “druggable”genes that regulate the mesenchymal signature in malignant glioma and totest them as candidate therapeutic targets (EXAMPLE 5); to assemble anddisseminate a genome-wide, Human Glioma interactome (HGi) that willintegrate the diverse sources of genetic, epigenetic, and functionalalterations that characterize the mesenchymal phenotype of high-gradeglioma (EXAMPLE 6). The HGi will be accessible to the scientificcommunity via the MAGNet Center dissemination infrastructure.Ultimately, the aim is to exploit the computationally inferred andexperimentally validated regulators of glioma aggressiveness asinvaluable new targets for therapeutic intervention.

Reconstruction of the Combinatorial Regulatory Program for theExpression of the Mesenchymal Signature of High-Grade Glioma andPhenotypic Analysis of its Disruption in GBM-BTSCs.

The goal of these experiments is the integration of the transcriptionalnetwork predicted by ARACNe, the post-translational interactionspredicted by MINDy, the binding data generated by ChIP-on-Chipexperiments, the proteomic TF-TF interaction experiments, and theexpression profile analysis of the changes after inactivation of Stat3and C/EBPβ TFs in GBM-BTSCs. By combining various data sources, the keyproteins required for MGES activation and maintenance will be uncoveredand a comprehensive view of the cellular network driving the MGES inmalignant glioma will be provided.

ARACNe analysis. ARACNe will be used with 100 rounds of bootstrapping oneach of four datasets (HGCM, HGEP_(Lee), HGEP_(Ph), and HGEP_(TCGA)) togenerate comprehensive high-grade glioma transcriptional networks (58;herein incorporated by reference in its entirety). TFs will beidentified based on their specific molecular function annotation in theGene Ontology. The analysis protocol described in Ref. 58 (hereinincorporated by reference in its entirety) will be followed toaccomplish the following:

Stat3 and C/EBP Target Identification

An exhaustive set of candidate targets of the Stat3 and C/EBP TFs willbe examined. The new and comprehensive set of targets will be used tofurther elucidate the role of these validated MRs in the direct andindirect control of mesenchymal genes and in the transformation of NSCs.TF-targets can have been missed due to the relatively small andheterogeneous sample dataset used to reconstruct the MGES controlnetwork shown in FIG. 1. Thus, integration of data from four datasetswill significantly increase the statistical power and usefulness of theapproach. Specifically, the HGCM will provide information oninteractions driven by non-physiologic perturbations, while the otherthree sets will provide information about physiologic transcriptionalresponse.

Identification of Additional Regulators of the MGES

Decreasing TF-target false negatives will greatly enhance our ability toinfer additional MRs, whose regulons can have been too small to assessenrichment when computed from the Aldape dataset. Additional metrics,other than FET, will be explored to rank candidate MRs, including targetdensity odds ratio, coefficient analysis from SLR (described in EXAMPLE2) and GSEA (49; herein incorporated by reference in its entirety).These metrics are not affected by regulon size and will provide a moreunbiased ranking than the FET. Inferred MRs will be assembled into theMGES Master Regulatory Module (MRM).

The identification of physical interactions between MRM TFs will provideus with valuable information to design further experimental validationsfor bioinformatics results. Although the detailed experimental plan willobviously depend on the nature of the interaction(s) that will bedemonstrated, the interactions between two activator TFs can be requiredfor full activation of the target mesenchymal promoters. Conversely,interaction(s) between an activator TF and a repressor TF can functionto restrain the activity of the activator TF bound to the DNA regulatoryregion of the mesenchymal promoters. Both overexpression and silencingexperiments will be appropriate to interrogate the consequences of TF-TFinteractions for the expression of selected mesenchymal genes and/or theentire MGES

Identification of Upstream Regulators

Additional TFs that are candidate upstream transcriptional regulators ofthe MRM TFs will be identified. If both genes are TFs, ARACNe cannotdetermine directionality. Thus, additional assays and analysis can benecessary, such as the identification of DNA binding site and ChIPassays.

Full Transcriptional Regulation Mapping

A complete transcriptional network will be inferred using ARACNe,involving TFs that are expressed in the cells of interest (EXAMPLE 6).

Evidence from the four datasets, as well as additional evidence sourcessuch as interaction databases, literature data, and interactions inorthologous organisms will be integrated (55; herein incorporated byreference in its entirety). The Bayesian evidence integration approachusing either Naíve Bayes classifiers or a Bayesian Network approach willbe used, depending on the statistical correlation of the cluesoriginating from each dataset (see Ref. 55; herein incorporated byreference in its entirety). The approach involves the use of establishedmachine learning methods. Additional integrative approaches, such asAdaboost (9; herein incorporated by reference in its entirety) will alsobe tested and compared to the Bayesian evidence integration approach.Based on prior work (36; herein incorporated by reference in itsentirety) and B cell interactome data (55; herein incorporated byreference in its entirety), one can expect that clues arising fromdifferent GEP sets will not be statistically independent and that aBayesian Network analysis can be needed. Positive and Negative Goldstandards will be based on evidence in TRANSFAC, other Protein-DNAinteraction databases, and ChIP assays. This will provide an idealcomplement of evidence from both tumor samples (heterogeneous context)and from the HGCM GBM-BTSC connectivity map (homogeneous context), thusallowing an ideal integration of TF-targets responding under diversephysiological and perturbation related stimuli.

MINDy Analysis.

MINDy (see EXAMPLE 2) will be used on the two datasets of sufficientsize (HGCM and HGEPTCGA) to generate an accurate and comprehensive mapof the interface between signaling proteins (including, among others,protein kinases, phosphatases, acetyltransferases, ubiquitin conjugatingenzymes, and receptors) and TFs. This work will replicate the equivalentmap generated for human B cells and will provide important clues aboutsignaling pathway conservation in distinct cellular contexts (100;herein incorporated by reference in its entirety). Appropriate metricswill be used to assess the quality of the results, including overlap ofpredicted interactions with protein-protein interaction databases andNetworKIN algorithm inferences (50, 100; each herein incorporated byreference in its entirety). Additional opportunistic assays will be usedto validate interactions of specific biological value. The analysis willbe used to:

Identify Modulators of MRM TF Activity

Upstream modulators of MRM TFs, including Stat3 and C/EBP will beinferred. Modulators that silence the MGES when inhibited providecandidate therapeutic targets and will be experimentally followed up inEXAMPLE 5. Conversely, modulators that activate the MGES genes wheneither inhibited or activated will provide candidate hypotheses forfocal gene loss or amplification in tumors, which will be searched fromthe TCGA-derived tumor Gene Copy Number platforms.

Identify Candidate Post-Translational Master Regulators of theMesenchymal signature of GBM

As discussed in Ref. 100 (herein incorporated by reference in itsentirety) MINDy can be used to associate a regulon* to each non-TFmodulator protein. This is an extension of the classical TF-regulonconcept to protein that directly or indirectly regulate one or more TFs.A regulon* represents the set of TF-targets indirectly regulated by aprotein via the TF(s) it modulates (the modulon). Ref. 100 (hereinincorporated by reference in its entirety) shows and biochemicallyvalidates that MINDy identified regulons* can be effectively used toidentify the signaling proteins targeted by an shRNA silencing assayfrom GEP differential expression before and after silencing. Thiseffectively validates the ability to infer post-translationally actingMRs. Specifically, MINDy will first be used to infer a regulon* for eachanalyzed signaling protein and then the MR approach will be applied todetermine significance of regulon* overlap with MGES genes. Signalingproteins whose regulon* is significantly enriched in MGES genes will be(a) considered candidate post-translational MRs, (b) experimentallyvalidated using siRNA assays, and (c) tested for genetic and epigeneticalterations.

Extension of the Enrichment Analysis

FET p-values are strongly dependent on datasets size. Additionalapproaches will be explored, such as the GSEA (92; herein incorporatedby reference in its entirety), as discussed in Ref. 49 (hereinincorporated by reference in its entirety). This requires a list L I ofavailable genes ranked by their differential expression between twophenotypes and a list L2 of genes of interest (i.e. the MGES). WhetherL2 is enriched in genes that are most up- or down-regulated in L1 willbe tested. Since GSEA corrects for gene set size, this will be lesssensitive to regulon/modulon size.

MINDy Extensions

MINDy is the first algorithm able to identify post-translationalmodulators of TF activity from gene expression profile data. However, ithas several limitations that can prevent specific modulators from beingidentified. MINDy uses an extremely conservative, Bonferroni-correctedsignificance threshold for the CMI analysis because of the large numberof tested modulator-TF-target triplets. Thus, some significant tripletscan be missed causing two problems: (a) increased false negatives amongTF-targets and (b) increased false negatives among inferred modulators.Less conservative threshold for triplet selection will be used andcompute a null hypothesis on the minimum number of significant tripletswith same TF and modulator, necessary to declare the modulator-TFinteraction statistically significant. This is similar to the notion ofstatistical enrichment in GSEA where a set of genes, each one withmodest p-value (i.e. not statistically significant on a single-genebasis), produces significant p-value for the gene set. In thepreliminary results, this approach was used to compute Stat3 and C/EBPβmodulators from 236 ATLAS/TCGA GEPs. Specifically, a threshold ofp<0.05, not Bonferroni-corrected, was used to select significantmodulator-TF-target triplets. The probability p(n) of observing nsignificant triplets with the same TF (e.g. Stat3) and modulator wascomputed. The null hypothesis model was generated by sample-shufflingbased CMI analysis. As discussed, this was highly effective indiscovering known modulators of Stat3. Additionally, modulatorsdiscovered by regular MINDy rank high among the larger set of modulatorsinferred by this more sensitive analysis (p<1E-4). While the newapproach infers many more modulators and modulator-dependent targets,and can have far fewer false negatives, the p-value computed bysample-shuffling can be less conservative. The plan is to correct thisproblem by exploring a variety of approaches to improve thenull-hypothesis generation, such as fitting distribution mixtures, anapproach shown to be highly successful in the study of ChIP-Chip data(57; herein incorporated by reference in its entirety). That the newanalysis reduces false negatives without substantially increasing falsepositives will also be validated. Additionally, exploration ofadditional multivariate metrics such as the information theoreticconcept of synergy S[TF; t; M]=1[TF; t; M]−1[TF; t]−1[TF; M] is planned.By replacing the CMI with synergy one can remove the limitation thatonly modulators that are statistically independent of the TF areinferred by MINDy. Since modulators and TFs can be part of regulatoryloops that affect their expression in coordinated fashion, this can alsolead to discovery of additional modulators.

Experimental Determination of the Combinatorial Mode of Action of theMesenchymal TFs in Human Glioma.

Yeast assays have shown that deletion of a TF affects only a relativelymodest subset of targets and fails to dramatically affect cellphysiology (24; herein incorporated by reference in its entirety).Without being bound by theory, combinatorial regulation by multiple TFscan be more specific and effective in activating and suppressingspecific genetic programs in the cell. Coherent FF loops, where two TFsshare the same targets and one regulates the other, arewell-investigated models to implement such redundant regulation logic.Several studies showed that coherent FF loops with an AND logic reducetransient noise in transcriptional regulation programs, since theirtargets are effectively regulated only through persistent signals.However, OR logic feed-forward loops can also compensate for the loss ofa single TF. Thus, it is important to address the role of the regulatorymotifs within the inferred MRM to discriminate their ability to filtertransient noise from that of providing transcriptional redundancy.Specifically, one behavior is associated with synergistic control (i.e.both TFs are required for target regulation) while the other isassociated with additive (i.e. compensatory) control (one TF compensatesfor the other but the effect is stronger in combination). Discriminatingbetween these two “regulatory logics” is important to understand diseaseetiology and determine appropriate therapeutic targets.

In EXAMPLE 2, it was shown that at least 80% of the regulatory regionsof the genes predicted as first neighbor of the mesenchymal TFs by theARACNe network are physically bound by the corresponding TFs (FIG. 3).However, individual binding assays fail to characterize the complexityof the regulatory region upstream of a gene providing only a lower-boundon the actual TF binding activity. Thus, the full scope of the directregulatory activity of the mesenchymal TFs for the mesenchymalsubnetwork can only emerge from genome-wide ChIP assays (ChIP-on-Chip).Since preliminary data indicate that Stat3 and C/EBPβ, are bothnecessary and sufficient to induce the mesenchymal signature genes, onecan obtain high-resolution maps of their genome-wide chromatininteractions by ChIP-on-Chip analysis.

The ChIP-on-chip Significance Analysis (CSA), a method for ChIP-Chipdata analysis, was recently described, which significantly improvesspecificity and sensitivity (57; herein incorporated by reference in itsentirety). For this reason, CSA is suited to identify regulatory programoverlap of multiple TFs. CSA was used to demonstrate that 93% of NOTCH1bound promoter also bound MYC (57; herein incorporated by reference inits entirety). This cannot be possible with methods yielding higherfalse negative rates. This analysis will provide a set of targets boundby both TFs, which can be interrogated in functional assays forsynergistic vs. additive regulatory control. Individual TF-DNA complexeswill be immunoprecipitated from the human “mesenchymal” glioma cell lineSNB75 (FIG. 2) and hybridize global tiled arrays (Agilent Technologies)covering promoter regions of annotated human genes (approx. 17,000genes). DNA microarrays contain 60-mer oligonucleotide probes coveringthe region from −8 kb to +2 kb relative to the transcription start sitesfor annotated human genes. This analysis will allow determination of thefull set of Stat3 and C/EBPβ-occupied genes in human glioma cells, aswell as their overlap. Consequently, one will be able to determinewhether, as predicted by original computational analysis, the promotersof the 136 mesenchymal signature genes are enriched among theStat3-C/EBPβ-occupied promoters in the genome. Although some TFsregulate genes from distances greater than 8 kb, 98% of known bindingsites for human TFs occur within 8 kb of target genes. For these assaysstate-of-the-art ChIP-on-Chip protocols and DNA microarray technologythat are known to minimize false positive rates will be used (12, 70;each herein incorporated by reference in its entirety). Most of theinitial ChIP-on-Chip experiments used genomic arrays comprised of PCRproducts that only allowed crude mapping of binding sites and oftenresulted in lower quality results. The more recent experimentalplatforms for these assays use oligonucleotide tiling arrays that allowfar higher resolution mapping of the binding regions by covering theregion where an interaction can be detected with multiple independentprobes, thus reducing both false positives and false negatives.

Biochemical and Computational Analysis

ChIP and ChIP-on-Chip experiments will be done according to theprotocols recently described (31, 41, 57, 70; each herein incorporatedby reference in its entirety). Bound genomic regions will be identifiedusing CSA, which has been shown to produce a 10-fold increase inbiochemically validated bound sites (57; each herein incorporated byreference in its entirety). For example, a global, genome-wide analysiscan exhaustively determine the full set of Stat3 and C/EBPβ-boundpromoters and establish whether the promoters of the 136 mesenchymalsignature genes are enriched among the Stat3-C/EBPβ-occupied promoters.Therefore, the ChIP-on-Chip experiments will be expanded to a global,genome-wide scale. Chromatin immunoprecipitation products will behybridized onto tiled arrays (commercially available from AgilentTechnologies) covering promoter regions of annotated human genes(approx. 17,000 genes). A method that significantly improves ChIP-Chipanalysis (ChIP-Chip Significance Analysis, CSA) will be carried out (57;each herein incorporated by reference in its entirety). CSA was used toshow the almost perfect overlap between promoters binding NOTCH1 and MYC(93% of NOTCH1 binding promoters also bind MYC). Because of its very lowfalse negative and false positive rate, CSA is uniquely suited to showthe overlap between Stat3- and C/EBPβ-bound promoters.

Briefly, this approach generates a much more realistic null hypothesisfor ChIP-Chip data by modeling the IP/WCE ratio (IP=Immunoprecipitatedprotein channel, WCE=whole cell extract channel) for unbound sites. Thisis done by fitting a non-parametric probability density to the left tailof the IP/WCE distribution, which is essentially not-affected by DNAbinding events, and using it to extrapolate the right tail of thedistribution to obtain a realistic p-value for rejecting thenull-hypothesis. The approach has led to the identification of almostperfectly overlapping transcriptional programs, such as those of theNotch1 and MYC TFs in T cells, overlapping on 1,668 of the 1,804 genesbound by Notch1 (92.5%, p-value=3.6×10⁻¹²). As a result, it will beuseful to determine the true extent and identity of the Stat3 and C/EBPtarget overlap. It will also provide high-accuracy bound sites that canbe interrogated using a variety of DNA binding site analysis tools, suchas DME (84-87; each herein incorporated by reference in its entirety) toidentify known TFs whose DNA-binding profiles matches are enriched inthe bound vs. unbound fragment as well as to discover new DNA-bindingprofiles de novo. Both approaches will be used to fully characterize thecis-regulatory modules that support the combinatorial regulation of thetargets by multiple TFs and to infer synergistic TF interactions. ThePromoclust tool (88; herein incorporated by reference in its entirety),which uses permutation pattern discovery across orthologous regulatorysequences in related organisms, will be performed to identify conservedcis-regulatory motifs comprising multiple DNA binding sites. This methodwill be applied to the analysis of the MGES genes to identify specificregions where TFs, including Stat3 and C/EBPβ can interact. This willidentify the sites mediating possible synergistic regulation byTF-complexes. Validation of promoter occupancy will be performed byquantitative PCR analysis of IP and their corresponding WCE as describedin recent publications (57, 70; each herein incorporated by reference inits entirety).

Combinatorial Regulation

As previously shown, ARACNe inferred targets of the MRM TFs are highlyoverlapping (see Table I). Without being bound by theory, some of theMRM TFs can form transcriptional complexes supporting a combinatoriallogic. To test this possibility immunoprecipitation assays for eachindividual TF followed by Western blot for any of the other candidatesynergistic TFs identified by ARACNe or by the cis-regulatory moduleanalysis will be performed. For most of the currently identified MRM TFs(Stat3, C/EBPβ, bHLHB2, and FosL2), antibodies are available and werevalidated in the ChIP assays shown in FIG. 3. For additional MRM TFs,including those emerging from the additional ARACNe and cis-regulatorymodule analysis, appropriate antibodies will be identified, whenavailable, and identical testing will be performed. Positive resultswill be further investigated by testing whether any of the candidateinteraction occurs directly through in vitro experiments in which one ofthe two TF, expressed as a GST-fusion protein, will be interrogated forits ability to capture the candidate interacting factors that had beensynthesized from a rabbit reticulocyte lysate. Without being bound bytheory, an interaction between an activator TF and a repressor TF canfunction to restrain the activity of the activator TF bound to the DNAregulatory region of the mesenchymal promoters. Overexpression andsilencing experiments of the genes coding for the TFs will interrogatethe functional consequences of TF-TF interactions for the expression ofselected mesenchymal genes and/or the entire MGES.

Stat3 and C/EBPβ as Targets to Impair Brain Tumor Formation.

It has been shown that constitutive expression of Stat3 and C/EBPβinduces the MGES in NSCs and confers them the ability to develop tumors(FIGS. 5-6). These findings establish that Stat3 and C/EBPβ aresufficient to promote mesenchymal transformation of NSCs. However, theultimate goal is to exploit the computationally inferred MRs asinvaluable targets for therapeutic intervention in malignant glioma.Without being bound by theory, functional inactivation of the drivers ofMGES in glioma collapse not only the gene expression signature but alsothe phenotypic hallmarks endowed by the signature, namely glioma tumoraggressiveness. This will be tested in GBM-BTSCs, a cellular systemmodeling human GBM in vitro and in vivo. Thus, Stat3 and C/EBPβ will bedepleted using a tetracycline regulatable lentiviral system (94; hereinincorporated by reference in its entirety) and the functionalconsequences of loss of Stat3 and C/EBPβ in GBM-BTSCs will be explored.Two assays—one determining the percentage of clone-forming neuralprecursors (clonogenic index) and the second assessing the expansion ofneural stem cell pool by growth kinetics analysis—will be used todetermine the consequences of Stat3 and C/EBPβ silencing on self renewalof GBM-BTSCs.

Next, the expression of CD133, a marker enriched in normal and tumorstem cells of the nervous system will be measured. Without being boundby theory, silencing of Stat3 and C/EBPβ will limit stem cell behaviorof GBM-BTSCs. Possible outcomes of silencing of Stat3 and C/EBPβ inGBM-BTSCs are growth arrest associated with differentiation along one ormultiple neural lineages or apoptosis. Therefore, the expression ofspecific markers for the neuronal, astroglial and oligodendrogliallineage will be determined, proliferation rate will be measured byimmunostaining for BrdU and apoptotic response will be tested by Tunelassay and Annexin V immunostaining. In order to obtain statisticallyrelevant results in vitro experiments will be conducted in at least fiveindependent GBM-BTSCs lines. The effects of Stat3 and C/EBPβ silencingon the tumor initiating capacity of GBM-BTSCs will be tested in vivo bythe transplantation of GBM-BTSCs into the mouse brain. Transplantationof GBM-BTSCs into the brain of immunodeficient mice generates highlyaggressive tumors displaying each of the phenotypic hallmarks of humanGBM (proliferation, anaplasia, tumor angiogenesis, necrosis, formationof pseudopalisades). Consistent with the notion that lentivirusesefficiently transduce neural precursors (94; herein incorporated byreference in its entirety), infection of more than 90% of GBM-BTSCscultures is routinely obtained. For silencing experiments, a smallhairpin RNA expression cassette targeting endogenous Stat3 and C/EBPβ(H1-Stat3 shRNA or H1-C/EBPβ shRNA) is inserted downstream of the tetOsequence. The advantages of this design in a single vector is tighttet-dependent regulation of either the transgene or the shRNA, a fast onto off or off to on kinetics and high levels of drug responsiveness (94;herein incorporated by reference in its entirety). Moreover, theconditional knockdown of the selected endogenous gene is mirrored by theexpression of GFP by the transduced cells, thus facilitating monitoring.

Transduction of GBM-BTSCs with lentiviruses will be performed followingprotocols established in the past for lentivirus-mediated transductionof NSCs and routinely used in our laboratory (11, 16; each hereinincorporated by reference in its entirety). The key aspect of GBM-BTSCscultures is the ability of such cells to maintain their stem cell statewhen grown as neurospheres in serum-free medium containing EGF and bFGF.To initiate exit from the stem cell state and promote differentiation,single cell suspensions will be cultured in the absence of serum andgrowth factors and allowed to adhere onto Matrigel-coated glasscoverslips. To analyze differentiation, cells will be fixed in 4%paraformaldeyde and processed for immunofluorescence of neural antigens.To evaluate tumorigenicity in the brain, lentivirally transduced BTSCwill be orthotopically transplanted following washing and resuspensionin PBS at the concentration of 10⁶ cells per ml (injection volume: 10μl).

To activate the expression of Stat3 and C/EBPβ shRNA, mice will betreated by oral doxicyclin. Ten mice per group will be injected andsurvival analysis will be established by Kaplan-Meyer Longrank test.Without being bound by theory, inactivation of mesenchymal TFs impairstumor formation and/or decreases migration and angiogenic capability.Similar experiments will be performed to ask whether enforced expressionof ZNF238 synergizes with silencing of positive TFs to trigger thecollapse of the MGES and suppresses the biological attributes of gliomaaggressiveness that are linked to this signature.

Example 4 To Elucidate the Mechanism of ZNF238 Silencing in High-GradeGlioma and Test the Role of ZNF238 Gene Loss in Gliomagenesis in theMouse

Somatic mutations affecting large TF hubs, controlling a large number oftargets, have been shown to be associated with cancer (26; hereinincorporated by reference in its entirety). Loss of multiple componentsand dysregulated expression and/or activity of key oncogenes and tumorsuppressor genes occur in most forms of cancer. ZNF238 is the only largeTF hub that emerged from the ARACNe analysis of GBM microarraycollection as a candidate repressor of the MGES. We found that ZNF238mRNA is markedly expressed in normal brain but undetectable in GBM (FIG.2). Similar patterns of expression of ZNF238 mRNA from an independentset of normal brain vs. GBM samples available from the Oncomine databasewere detected (FIG. 9). Furthermore, ZNF238 can play important roles fordifferentiation of neural cells in the brain (8). ZNF238 codes for a522-amino acid protein (also called RP58) that contains a N-terminal POZdomain displaying homology with the POZ domain of Bcl-6 and four sets ofKruppel-type C2H2 zinc fingers. It associates with condensed chromatinwhere it recruits the Dnmt3a DNA methyltransferase and is thought tofunction as a DNA-binding protein with transcriptional repressionactivity (2, 23; each herein incorporated by reference in its entirety).

Given the high degree of connectivity in the ARACNe inferred networkbetween ZNF238 and the MGES targets and the significant target overlapbetween ZNF238 and the positively acting mesenchymal TFs, loss of ZNF238expression and/or activity is essential to release the normal constrainsimposed on the regulatory regions of the MGES genes. Without being boundby theory, loss of ZNF238 in GBM compared to normal brain indicates thatloss of ZNF238 is a necessary step in tumor progression. However, thecomputational and expression data cannot discriminate whether loss ofZNF238 is sufficient or concurrent overexpression of Stat3 and C/EBPβ isalso needed to initiate glial tumorigenesis along the mesenchymalphenotype. To test this, the expression of ZNF238 between tumors derivedfrom Stat3-C/EBPβ-expressing NSCs and the same cells cultured in vitroby qRT-PCR as been compared. Interestingly, ZNF238 was markedlydown-regulated in the tumor cells in vivo (FIG. 10). This finding raisesthe intriguing possibility that cells expressing Stat3 and C/EBPβrequire ablation of ZNF238 before they emerge into tumors. Furthermore,siRNA-mediated knockdown of ZNF238 in NSCs expressing Stat3 and C/EBPβled to significant up-regulation of mesenchymal signature genes, thusproviding further validation to our finding that ZNF238 is a powerfulrepressor of the MGES (FIG. 11). Interestingly, the gene encoding ZNF238maps to chromosome 1q44, a region that is sporadically deleted in humanbrain tumors (8; herein incorporated by reference in its entirety).

In summary, the ZNF238 gene in NSCs expressing Stat3 and C/EBPβ has beenknocked down, and decrease of ZNF238 derepresses the expression ofselected mesenchymal signature genes has been shown (Serpinel, PLAUR,Col4A1, see FIG. 11). These findings validate that ZNF238 operates asrepressor of mesenchymal signature genes. To further validate thatZNF238 operates as a new tumor suppressor gene in brain tumors, it isshown that: i) ZNF238 is markedly down-regulated in the tumors derivedfrom Stat3-C/EBPβ expressing NSCs (FIG. 10); ii) From the analysis of anindependent set of glioblastoma multiforme samples from the Oncominedatabase for the expression of ZNF238, it was discovered that thesehuman tumors display a significantly reduced expression of ZNF238, whencompared with the expression of ZNF238 in normal brain (FIG. 9). Takentogether, the new data functionally validate the notion that ZNF238 is atranscriptional repressor of mesenchymal signature genes and strengthenthe rationale for the generation of the conditional knockout mouse ofZNF238 in the neural tissue. The systems described herein determinewhether ZNF238 is a true tumor suppressor gene for neural tumors andwhether it functions to repress the expression of the mesenchymalsignature in vivo.

In this example, whether ZNF238 is required to restrain the activity ofthe MGES in the brain will be examined and whether loss of ZNF238 is atumor-initiating event in neural cells will be asked. The mechanism(s)of ZNF238 loss in primary glial tumors will be identified through anintegrated search of genetic and epigenetic alterations. The specificrequirement for ZNF238 in the suppression of malignant transformationwill be examined by ablating ZNF238 in the mouse brain. Once generated,ZNF238 mutant mice will be used to ask whether loss of ZNF238 isgliomagenic per se or requires collaborating lesions and evaluatewhether concurrent overexpression of ZNF238 target genes contributes totumor formation. Specifically, whether loss of ZNF238 expression leadsto overexpression of the other TFs in the MRM, whether the opposite istrue, or whether the two events are independent and both required foroncogenesis will be determined. Finally, GEPs of genetically distincttumors and cross-species comparisons will be assembled to identify thegenetic components necessary to reconstruct the human GBM mesenchymalsignature in the mouse. Final outcome of the study will be to establishbrain tumor models in which we will test the vulnerability tomulti-target intervention strategies. As for Stat3 and C/EBPβ, the HGCM,HGEP1, and HGEP2 dataset will be used to create a repertoire of ZNF238co-factors and upstream regulators, using the same methodology discussedin EXAMPLES 2-3.

ZNF238 as a tumor suppressor gene in high-grade glioma. Differentgenetic and/or epigenetic mechanisms can operate, alone or incombination, to silence ZNF238 gene expression in malignant glioma.First and foremost, the ZNF238 gene can be targeted by direct geneticalterations (deletion, recombination such as internal duplication ortranslocation and mutation). These alterations can specifically targetthe ZNF238 gene (e.g. point mutations) or be broad and involve alsoadjacent loci. Furthermore, they can cooperate with other epigeneticalterations to effectively silence the two ZNF238 alleles. A prioranalysis of the genetic platforms available from the ATLAS TCGA networkdid not identify major rearrangements in the ZNF238 locus. However,focal alterations of the ZNF238 gene can only be excluded after completeresequencing of the corresponding genetic locus in a significant numberof brain tumors samples. Furthermore, it is recognized that, in theabsence of changes in the coding region, genetic alterations in theZNF238 regulatory region (promoter) can knock out a crucial enhanceractivity for ZNF238 mRNA expression in the nervous system. Therefore,beside the analysis of the ZNF238 coding region, the analysis will haveto include the full ZNF238 promoter. The relevance of ZNF238 promotertargeting in brain tumors is underscored by the preliminary finding thatthe ZNF238 promoter is aberrantly methylated in glioma cells (FIG. 12).

Promoter methylation is a frequent mechanism for inactivation of tumorsuppressor genes in human tumors and it will be explored in the nextparagraph. Here, whether the ZNF238 promoter and/or its coding sequenceare targets for broad or focal alterations in malignant brain tumors bydouble strand sequencing of tumor DNA is considered. The availability of200 frozen GBM specimens harvested from anonymous donors and stored inthe brain tumor bank of the Columbia Cancer Center Tissue Bank will betaken advantage of. The ZNF238 gene in the 18 human glioma cell linesavailable in the laboratory will be sequenced. The entire ZNF238promoter (4,000 by upstream of the transcription start site) and codingregion from genomic DNA derived from 200 GBM specimens will besequenced. The primer pairs required for successful PCR amplificationfollowed by direct double-strand sequencing coverage have beenvalidated. Functional experiments will validate the significance of anyZNF238 mutation identified in the sequencing screen. The type of geneticmutation that will be detected in brain tumors can immediately directone towards the functional consequences produced by that genetic event.However, subtle mutations in putative TF-binding sites in the ZNF238promoter (e.g. point mutations) are detected, experiments will bedesigned to establish the consequences of the mutation on ZNF238promoter activity by using luciferase-reporter assays. The assays willbe conducted by preparing plasmid constructs in which the wild typeZNF238 promoter and the corresponding mutant(s) will be placed in frontof a luciferase reporter gene. This system allows accurate quantitationof promoter activity and is ideally suited to identify the partialreduction of ZNF238 promoter activity that can be associated withcertain mutations in TF-binding sites. Execution and evaluation ofpromoter-luciferase assays have been shown (31, 41; each hereinincorporated by reference in its entirety). An alternative/complementarymechanism to the direct genetic inactivation of ZNF238 can includegenetic/epigenetic targeting of upstream regulators of ZNF238. ARACNecan be used to infer TFs that are candidate upstream regulators ofZNF238, as described in EXAMPLE 3. A similar experimental plan will beimplemented to search for alterations in the genes coding for thesemodulators. The availability of the ATLAS TCGA genetic platforms will beinstrumental to identify/exclude major rearrangements.

Analysis of Promoter Methylation of ZNF238.

Computational and expression predictions converged towards theidentification of a highly vulnerable structure of the regulatory regioncontrolling ZNF238 expression. The ZNF238 promoter/enhancer is unusuallyrich in evolutionarily conserved CpG islands (FIG. 12A), which aretargeted by DNA methyltransferases leading to gene expression silencing.Methylation of regulatory DNA regions is a common mechanism in humancancer and is implicated in the constitutive silencing of tumorsuppressor genes in malignant glioma (109; herein incorporated byreference in its entirety). Thus, whether promoter methylation inducessilencing of ZNF238 was considered. Pharmacological inhibition ofmethylation with an inhibitor of DNA methyltransferases (5-Azacytidine)elevated the expression of ZNF238 mRNA in the T98G glioma cell line(FIG. 12B) and repressed the expression of SerpineH1 and CH3IRL1 (FIG.12C), two mesenchymal genes predicted as ZNF238 targets by ARACNe (FIG.1). These results indicate that the aberrant methylation of the ZNF238promoter can account for silencing of ZNF238 expression in primary GBM.

The extent by which the ZNF238 promoter is aberrantly methylated in thecollection of 200 human GBM will be determined. Methylation status ofthe promoter regions of ZNF238 will be analyzed by matrix-assisted laserdesorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) ofPCR-amplified, bisulfate-modified high grade glioma DNA, as previouslydescribed (Sequenom, San Diego, Calif.) (19, 89; each hereinincorporated by reference in its entirety). This method allowssemiquantitative, high-throughput analysis of methylation status ofmultiple CpG units in each amplicon generated by base specific cleavage.The PCR product is cleaved U specifically. A methylated template carriesa conserved cytosine, and, hence, the reverse transcript of the PCRproduct contains CG sequences. In an unmethylated template, the cytosineis converted to uracil. The reverse transcript of the PCR producttherefore contains adenosines in the respective positions. The sequencechanges from G to A yield 16-Da mass shifts. The spectrum can beanalyzed for the presence/absence of mass signals to determine whichCpGs in the template sequence are methylated, and the ratio of the peakareas of corresponding mass signals can be used to estimate the relativemethylation. This assay enables the analysis of mixtures without cloningthe PCR products.

The ZNF238 gene contains a large CpG island of approximately 2 kB thatlies upstream of the coding region. Four independent amplicons thatcover the entire region (#1, −3576 to −2894; #2, −2878 to −1643; #3,−1619 to −1416; #4, −1197 to −1090) will be analyzed. Methylation datawill be viewed in GeneMaths XT v 1.5 (Applied Maths, Austin, Tex.).Similar approaches will be used to investigate co-factors and upstreamregulators of ZNF238 that can emerge from the ARACNe analysis. Studiesof the mechanisms of inactivation of tumor suppressor genes in primarytumors have been described (15, 32, 69; each herein incorporated byreference in its entirety) and, depending on the outcomes of the initialexperiments, specific experimental strategies will be designed tovalidate the significance of genetic and/or epigenetic inactivation ofZNF238 in GBM and of any additional negative regulator of themesenchymal signature genes emerging from the analysis.

Analysis of the Functional Effects of ZNF238 Expression in Glioma Cells.

A fundamental assay to test whether a gene has tumor suppressor functionis its ability to inhibit tumor growth when re-introduced in cancercells. Thus, whether ZNF238 fits this criteria will be evaluated byre-expressing the ZNF238 gene in the human glioma cell lines that lackendogenous expression of ZNF238. Through the use of atetracycline-inducible system, the impact of ZNF238 expression for theMGES will be evaluated and the following functional experiments will beperformed: i. Evaluate the effect of ectopic ZNF238 expression on cellproliferation in the glioma cell lines SNB75, T98G and SNB19. Likeprimary GBM, none of the three cell lines express detectable amounts ofZNF238 mRNA (FIG. 2). The effects of ZNF238 expression for proliferationwill be tested by colony assays, cell counting, BrdU incorporation andFACS analyses; ii. Ask whether reconstitution of ZNF238 expression inglioma cells perturbs the ability to migrate and invade through theextracellular matrix using the in vitro and in vivo assays shown inFIGS. 5-6. These are the major phenotypic features of the MGES andsimilar experiments will also be done in the context of concurrentsilencing of one or more of the positively connected “mesenchymal TFs.”Any additional key tumor suppressor gene candidate, emerging from thecomputational analysis, will be tested using similar approaches. In casea hierarchical control structure emerges from the analysis, one canstart by validating the genes that are most upstream in the regulatorylogic.

Generation of Mice Carrying a Conditional Mutant Allele of ZNF238.

Although in vitro experiments can provide valuable insights, thevalidation of ZNF238 as repressor of the MGES and glioma tumorsuppressor gene comes from the genetic analysis of ZNF238 function invivo. Therefore, one can develop a ZNF238 allele (ZNF238Flox) thatcontains LoxP sites flanking exons 1 of the mouse ZNF238 gene (FIG. 13).Exon 1, which contains the entire ZNF238 coding sequence, is deletedafter expression of Cre recombinase to generate a ZNF238 null allele.Once the appropriate constructs have been generated and sequenceverified, the final targeting vector is electroporated into mouseembryonic stem cells (ES) and, after G418 selection, ES colonies will bescreened for recombination events by Southern blotting and PCR.Appropriate clones will be used to generate chimeric mice bymicroinjection into C57BL/6 blastocysts. F1 animals will be screened forgerm line transmission of the mutant ZNF238 allele by tail-DNAgenotyping. This will involve direct sequence of PCR products as well assouthern blotting to demonstrate ablation of ZNF238. The primary focuswill be to establish the function of ZNF238 in the nervous system. Toachieve specific inactivation of the ZNF238 in the nervous system,ZNF238Flox mice will be crossed with the GFAP-Cre deleter strains togenerate GFAP-ZNF238Flox. GFAP-Cre mouse strains are already availablein our facility. Conditional knockout mouse models have recently beengenerated for three different genes (Id2, Id1 and Huwe1) and one isfully equipped to generate this new genetically modified mouse. Othermouse tumor models based on Cre-mediated recombination have beengenerated and tested (51, 52; each herein incorporated by reference inits entirety).

Analysis of GFAP-ZNF238 Conditional Mutant Mice to Address the Role ofZNF238 Loss in Tumor Development in the Brain.

The GFAP promoter is active in most embryonic radial glial cells thatexhibit neural progenitor cells properties and mature astrocytes (53,54, 67, 112; each herein incorporated by reference in its entirety).Early onset of the activity of the GFAP promoter in progenitor cellsleads to Cre-mediated recombination in early neural cells as well astheir progeny, including a large array of neural stem/progenitor cellsin the sub-ventricular zone of the adult mouse as well as in matureneurons, astrocytes oligodendrocytes and cerebellar granule neurons (53,54, 59, 62, 97, 112; each herein incorporated by reference in itsentirety). One can compare the tumor initiating potential of ZNF238 losswith or without mutation in tumor suppressor gene NF1. The choice isbased on the following data: 1) Individuals afflicted withneurofibromatosis type 1 (NF1) are predisposed to malignant astrocytomain the brain (80; herein incorporated by reference in its entirety), and2) Mice carrying NF1 loss in the GFAP-positive compartment in the brain(GFAP-Cre; Nf1 flox/flox) exhibit increased numbers of brain and opticnerve astrocytes, but they do not develop gliomas (5; hereinincorporated by reference in its entirety). Therefore, they represent amodel system to identify a specific role for loss of ZNF238 intransformation of neural cells. Nf1 flox mice are available through theNCI Mouse Models of Human Cancer Consortium. Additionally, one canconsider other candidate oncogenes and tumor suppressor genes emergingfrom the MGES transcriptional program modeling effort described earlier.

ZNF238Flox mice will be crossed with hemizygous GFAP-cre transgenic mice(38; herein incorporated by reference in its entirety), generatingGFAP-ZNF238Flox mice and then bred to appropriate strains to yieldGFAP-ZNF238Flox; Nf1Flox/Flox progeny for the analysis. Genotyping ofZNF238 and NF1 alleles will be performed by PCR. Offspring withconditional mutation of ZNF238 will be examined for neural defects. Ifthe ZNF238 mutant mice develop differentiation and/or proliferationabnormalities, one can use gene expression microarray to determinewhether such abnormalities are sustained by deregulated activity of theMGES in vivo.

One can determine the kinetics of tumor formation by daily clinicalexamination and serial pathology. Adult mice will be monitored fordevelopment of tumor associated signs and sacrificed appropriately.Tumor tissue will be isolated, fixed for immunostaining and frozen forDNA/RNA/protein analysis. Tumor latency, penetrance andhistopathological features will be monitored. Pathological examinationwill include, H&E for morphology, BrdU for proliferative index, andTunel for apoptotic rates. Immunohistochemical marker analysis for GFAP,NeuN and Synaptophysin will be used to confirm or rule out glial orneuronal lineage of the tumor, respectively. Further characterizationwill include Nestin immunohistochemistry to uncover NSCs and early glialprogenitors. Whenever possible, cell lines will be derived from tumorsfor biochemical analysis or explant studies. A key objective of thestudies is to perform a transcriptomic microarray analysis of the tumorsamples to generate a map of the mesenchymal signature in differentbiological states. To determine the extent to which mouse cancersexpress the GBM mesenchymal signature in a manner resembling the humantumors, the genes in the GBM mesenchymal signature will be used tocluster the mouse tumor data set hierarchically.

To determine whether there is hierarchical causal function of themesenchymal TFs for tumor formation in a ZNF238-null background, thegenes coding for each mesenchymal TF will be ectopically expressedeither individually or in combination by in vivo electroporation ofretroviral vectors. The requirement of these same genes will be testedby stably decreasing their expression in vivo with short-hairpinRNA-mediated interference (RNAi) lentivirus. Lentiviral and retroviralvectors for gene expression or silencing that co-express GFP areroutinely used. These vectors will allow one to track infected cells.Tumors will be examined for histology and gene expression profiling.Collectively, results from these experiments will reconstruct in vivothe mode of cooperation of ZNF238 with the mesenchymal TFs for MGESexpression and brain tumor formation.

Without being bound by theory, GFAP-ZNF238LoxP mice will developproliferative alterations in the brain and loss of NF1 accelerates tumorformation and/or increase malignancy. It has been shown that the onlyproliferating cells in the adult mouse brain are those in the SVZ (18;herein incorporated by reference in its entirety). Therefore, thisextremely low background will permit a sensitive survey of the brain forproliferating cells by BrdU incorporation. Further analysis of theregulatory control responsible for differentiating ZNF238 knock-out miceexpression from expression in high grade glioma can provide additionalinsight on key co-factor of this TF required for oncogenesis.

Example 5 To Computationally Identify and Biochemically Validate“Druggable” Proteins and Co-Factors that Modulate the MesenchymalSignature in GBM

Without being bound by theory, MGES genes will be dysregulated byseveral processes, including epigenetic silencing, gene copy numberalterations, regulation by additional TFs missed by the preliminaryanalysis, and genetic/epigenetic alterations of regulators upstream ofthe identified regulatory module. For the latter, one can especiallyfocus on modulators upstream of Stat3, C/EBPβ and ZNF238. For instance,to become transcriptionally competent, Stat3 must be converted to itsactive form by tyrosine kinase-mediated phosphorylation events (21, 34;each herein incorporated by reference in its entirety). Thus, targetingsome of the kinases in this pathway can suppress Stat3 phosphorylation,ablating its transcriptional activity.

In this example, one can (a) investigate complementary approaches toidentify candidate pharmacological targets and compounds for MGESsilencing and (b) validate their ability to reduce the aggressivephenotype of high-grade gliomas. A first more “targeted” approach willinvestigate specific upstream modulators of Stat3, C/EBP, ZNF238, andother MGES MRs from EXAMPLES 2-4. The second approach will use theHigh-grade Glioma Connectivity map (HGCM) to investigate druggableproteins as candidate MGES modulators. Druggable proteins will beidentified using the Druggable Genome database (30; herein incorporatedby reference in its entirety). Candidate targets will first beprioritized and screened in silico and then tested in vitro using siRNAsilencing assays. The targets emerging from this analysis will also betested for synergism to model the combinatorial regulation of the MGES.Finally, one can use several computational, literature-based, andexperimental approaches to identify compounds that can target the MGESmodulators identified by this analysis and test them in vitro and invivo for the ability to block glioma cell proliferation and invasion.

Targeted approach. One can start with a collection of (a) MINDy inferredcandidate modulators of the MGES regulatory module's TFs (see EXAMPLE 3)and (b) candidate MRs of the MGES genes inferred by the regulon*-basedMRA (see EXAMPLE 3). Inferred modulators will be first filtered, usingthe Druggable Genome database (30; herein incorporated by reference inits entirety), to identify Candidate Pharmacological Targets (CPT) andassociated compounds. In the MYC modulator analysis, ˜50% of the 30highest-confidence MINDy inferred modulators were bona fide MYCmodulators in vitro (101, 102; each herein incorporated by reference inits entirety). This is a lower bound, because the untested genes caninclude additional modulators. One can use the statistics defined inRef. 101, 102 (each herein incorporated by reference in its entirety) toidentify high-confidence candidate modulators of the MGES MRs andappropriate statistics will be developed to infer equallyhigh-confidence candidate MGES MRs using the regulon*-based approach.

Validation will proceed in two steps and will be used to inform the“unbiased” approach described herein. Modulators will be divided in twocategories, depending on biological activity. TF activators will includegenes that increase the TF's transcriptional activity while antagonistswill include genes that repress it. Since most drugs act as substrateinhibitors, only activators of the MGES positive regulators (e.g. Stat3and C/EBPβ) and antagonists of MGES negative regulators (e.g. ZNF238)will be considered. Similarly, for genes inferred by modulon-analysis,only MGES activators will be considered, such that their chemicalinhibition can result in down-regulation of the signature. Based onprevious analyses, and without being bound by theory, about 30-50candidate targets could emerge from this analysis. One can use atwo-step screening approach to minimize cost and maximize changes forcorrect target identification. In the first phase, one can pool siRNAsdirected against three sequences to silence each one of the candidatetargets and can perform qRT-PCR to validate suppression of thecorresponding target mRNA. Samples showing substantial (>70%) reductionin mRNA level will be hybridized to Illumina arrays in duplicates. Onecan then compute the GSEA enrichment of differentially expressed genesagainst the MGES to determine the contribution of silencing candidatetargets to MGES abrogation. Furthermore, use of two replicates canprovide adequate power to test enrichment of a large TF signature,including 50 to several hundred targets. Without being bound by theory,a smaller number of candidate modulators will show significantrepression of the MGES. These will be validated using the individualsiRNAs in the pool and additional siRNAs, if available, to excludepossible off-target effects. Specifically, one can test that siRNAs thatinduce silencing of the target modulator will show a consistentrepression of the MGES. Finally, one can test the effect of compoundsthat are reported in the database as active on specific targets emergingfrom this analysis.

Unbiased Approach.

The availability of the HGCM from EXAMPLES 2 and 3 will informapproaches tested in the MCF7 breast cancer cell line (48; hereinincorporated by reference in its entirety). A key advantage of thisapproach is that candidate druggable targets will be tested directlyagainst the MGES, without requiring interaction map inference. Thus, itcan provide targets whose connectivity can not have been appropriatelyreconstructed by ARACNe or MINDy. FIG. 14 illustrates the process forone candidate druggable target gene. This will be repeated exhaustivelyfor every candidate gene.

For example, if g_(DT) is a CPT in the druggable genome database (30;herein incorporated by reference in its entirety), the following stepswill determine if g_(DT) is a candidate MGES activator and thus acandidate target for pharmacological inhibition:

Step 1.

One can first rank-sort the profiles in the HGCM according to theexpression of gDT. Since perturbation assays were performed on a singlecell line, modulation of g_(DT) can be, on average, the dominant effect,i.e., induced by the chemical perturbation rather than by phenotypicassay variability. The first N profiles will thus represent assays wherethe perturbation induced transcriptional repression of g_(DT). This canbe called the G↓_(DT) set. Conversely, the last N profiles willrepresent assays where the perturbation induced transcriptionalactivation of g_(DT). This second set can be called the G↑_(DT) set.

Step 2.

One can then assemble a list L of genes ranked according to the t-teststatistics computed between the G↓_(DT) and G↑_(DT) sets. N can bechosen to be large enough so that g_(DT)-independent processes areaveraged out over the N samples, akin to mean field theory approaches inphysics, yet small enough so that average expression of g_(DT) isstatistically different. This is similar to the corresponding setselection in MINDy (see EXAMPLES 2-3; where we show that choosing N tobe about ⅓ of the total profile population produces optimal results). Inthis case, since true positive (TP) and false positive (FP) modulatorsbiochemically validated will be available, one can select N such that itproduces optimal recall and precision. One can compare the analyticallyand empirically derived values.

Step 3.

One can finally measure the MGES gene enrichment against differentiallyexpressed genes in L, using the GSEA method. This allows one to treatthe two sets, G↓_(DT) and G↑_(DT), as “virtual” g_(DT) perturbations andthe list L as the specific signature that results from thatperturbation. In FIG. 14, genes that are activated in the MGES are shownas short, blue, vertical lines. Repressed genes are shown as short, red,vertical lines. GSEA analysis will pinpoint g_(DT) selections that willrespectively enrich the blue genes among genes that are upregulated inG↑_(DT) and enrich red genes among genes that are downregulated in G↓DT.This approach was used preliminarily to test which druggable genesinduce apoptosis in MCF₇ cells using published connectivity map data(40; herein incorporated by reference in its entirety). It was shownthat known apoptosis inducing genes, such as the heat shock proteinHSP38, were highly enriched among the top modulators inferred by theapproach. Furthermore, testing of 8 high-ranking genes not previouslyassociated with apoptosis, using known chemical inhibitors, identifiedtwo compounds that induce apoptosis in vitro with IC₅₀ in thehigh-nanomolar to low micromolar regimens.

Apoptosis as a consequence of MGES Silencing.

While MGES recapitulates the hallmark of aggressive high-grade glioma,MGES genes are not completely overlapping with the genes that aredifferentially expressed upon co-silencing of Stat3 and C/EBPβ inGBM-BTSCs. As shown in FIG. 15, such co-silencing produces a markedlyapoptotic phenotype, as demonstrated by immunostaining for caspase 3,which can recapitulate tumor oncogene-addiction properties (104; hereinincorporated by reference in its entirety). Thus, the analysis ofco-silencing of Stat3 and C/EBPβ versus vector transduced controls, willallow one to generate a differential expression signature, distinct fromthe MGES signature, which will recapitulate the specific effects ofknockdown of Stat3 and C/EBPβ in glioma cells. This signature will beused, in addition to the MGES signature, for analyses to identifyadditional candidate druggable targets that can implement the desiredpro-apoptotic phenotype. It will also be used to test the accuracy andproperties of the Master Regulator Analysis method (MRA). Without beingbound by theory, MRA analysis can predict Stat3 and C/EBP as the MRs ofthe experimentally induced transformation event. This will allow one toexplore alternative metrics for MR ranking purposes and validate themethod.

Experimental Validation of MGES Modulators.

Once a repertoire of post-translational modulators of the MRM TFs isidentified, they will be first prioritized and validated biochemicallyin this example and then their biological function will be examined. Therepertoire of post-translational modulators will provide a context forthe rapid identification of targets of therapeutic value for thesuppression of the MGES.

Three distinct but highly integrated approaches will be used:

a) Constitutive and Inducible Expression of Individual Genes

Individual genes that appear to have a critical role in the regulationof MRM TFs will be tested for their ability to influence the regulationof the module through the tetracycline inducible lentiviral systemdescribed in EXAMPLE 3. This experimental system has been repeatedlyvalidated with GBM-BTSCs.

b) Inhibition of Individual Gene Expression Via Lentivirus-MediatedshRNA Transduction

The use of shRNAs to inhibit the expression of target genes intransduced cells has been established as the method of choice forablating the function of individual genes in somatic cells. In EXAMPLE2, it is shown that shRNA-mediated gene silencing in GBM-BTSCs can besuccessfully achieved through lentiviral-mediated transduction (see forexample the analysis of the effects of silencing Stat3 and C/EBPβ inGBM-BTSCs shown in FIG. 7).

One can use these experimental systems to examine whether i)overexpression of candidate activators of Stat3 and C/EBPβ in NSCsenhances mesenchymal and invasion phenotype in vitro as assayed byimmunofluorescence for mesenchymal markers (e.g. SMA, fibronectin,YKL40) and invasion assay and is gliomagenic in vivo followingstereotactic injection into the brain; ii) silencing of candidatemodulators of Stat3 and C/EBPβ in GBM-BTSCs diminishes the expression ofmesenchymal markers, decreases migration and invasion in vitro andinhibits the gliomagenic phenotype in vivo. Similar experiments will beperformed to examine the effects of overexpression or silencing ofcandidate modulators of ZNF238.

c) Pharmacological Inhibition of Specific Targets

An increasing number of pharmacological inhibitors of specific proteinsare becoming available. Although some of these inhibitors are notentirely specific for individual gene products, a sizable fraction isused with significant specificity. These pharmacological inhibitors willbe very useful experimental tools for the blockage of specific targetsand validation of their potential use as therapeutic targets in vivo.

Example 6 To Assemble a Human Glioma Interactome (HGi) IncludingTranscriptional, Signaling, and Complex-Formation Interactions

There are three main types of utilization: 1) one can make the HumanGlioma interactome (HGi) available to the research community using thesame geWorkbench infrastructure used for the Human B Cell interactome.This will allow the research community to interrogate the HGi toretrieve transcriptional and post-translational interactions for anygene of interest and to identify sub-networks in the HGi that aredifferentially regulated in various disease sub-phenotypes; 2) one canintegrate the HGi with our master regulator analysis tools, alsointegrated in geWorkbench, to allow the analysis of master regulators ofother phenotypes, E.g. low-grade/high-grade vs. normal, rather thanhigh-grade vs. low-grade, which is the subject of this proposal; 3) byextending the IDEA algorithm, one can allow using the HGi as anintegrative tool to combine diverse sources of evidence about genetic,epigenetic, and functional alterations to discover sub-networks that aredysregulated within specific sub-phenotypes of interest and to dissectthe mechanism of actions of commonly used anti-cancer compounds in thesecells.

Recent work has shown that context-specific interactomes can beeffectively used as integrative tools to dissect mechanisms ofdifferential regulation/dysregulation in normal and pathologic humanphenotypes (49, 55; each herein incorporated by reference in itsentirety). In this Example, one can assemble a computationally inferred,biochemically validated interactome for high-grade glioma and use it asa reference anchor to integrate the genetic, epigenetic, and functionaldata produced by different GBM-related studies. One can integrate datafrom the ATLAS/TCGA effort, including expression profiles, gene-copynumber alterations, promoter hyper and hypo-methylation, and sequence.To assemble the HGi, one can extend the evidence integration methodologydescribed in the attached Ref. 55 (herein incorporated by reference inits entirety). The HGi will include protein-DNA (PD) and protein-protein(PP) interactions specific to glioma cells. The latter include stable(i.e., same-complex) as well as transient (i.e., signaling)interactions. The HGi will be generated by applying a Naïve BayesClassifier to integrate a large number of experimental and computationalevidence.

Appropriate positive and negative “gold-standard” references will beassembled from curated databases, as also described in EXAMPLES 2-5.Evidence sources will include: the four expression profiles defined inEXAMPLES 2 and 3, literature data-mining from Gene Ways (83; hereinincorporated by reference in its entirety), TF-binding-motif enrichment,orthologous interactions from model organisms, and reverse engineeringalgorithms, including ARACNe and MINDy for regulatory andpost-translational interaction inference. For each evidence source, aLikelihood Ratio (LR) will be assessed using the positive/negative goldstandards. Individual LRs will then be combined into a global LR foreach interaction. A threshold corresponding to a posterior probabilityp≧0.5 will be used to qualify interactions as present or absent. It isimportant to notice that, given the infrastructure for the assembly ofcellular networks implemented by the MAGNet center, one will be able toaccess a large variety of data sources and algorithms that, otherwise,requires a significant effort to organize and coordinate.

Stable Protein-Protein Interactions.

A Positive Gold Standard (PGS) for PP interactions will be generatedusing 27,568 human PP interactions from HPRD (76; herein incorporated byreference in its entirety), 4,430 from BIND (4; herein incorporated byreference in its entirety), and 3,522 from IntAct (29; hereinincorporated by reference in its entirety). These originate fromlow-throughput, high-quality assays. The resultant PGS will have 28,554unique PP interactions between 7,826 gene-products (after homodimerremoval). The Negative Gold Standard (NGS) will include gene-pairs forproteins in different cellular compartments, resulting in a large numberof gene pairs with low probability of direct physical interaction. Pairsin the NGS that are also included in the PGS will be removed from theNGS. PP interactions will be inferred from the following source: (a)Interactions in the HPRD (76; herein incorporated by reference in itsentirety), IntAct (29; herein incorporated by reference in itsentirety), BIND (4; herein incorporated by reference in its entirety)and MIPS (63; herein incorporated by reference in its entirety)databases for four eukaryotic organisms (fly, mouse, worm, yeast); (b)human high-throughput screens (82, 91; each herein incorporated byreference in its entirety); (c) Gene Ways literature data miningalgorithm (83; herein incorporated by reference in its entirety); (d)Gene Ontology (GO) biological process annotations (3; hereinincorporated by reference in its entirety); (e) gene co-expression datafrom the HGSS, HGES1, and HGES2 expression profiles; and (e) Interproprotein domain annotations (64; herein incorporated by reference in itsentirety).

To simplify prior computation, evidence sources will be represented ascategorical data (i.e., continuous values will be binned as necessary).Only genes that are both expressed in the glioma expression profileswill be tested for potential interactions. Multiple methods to test forgene expression are being developed, including: (a) standard coefficientof variation analysis (e.g., cv >0.5), (b) methods based on thecorrelation of multiple probes within Affymetrix probeset for the samegene, and (c) information theoretic approaches based on the ability tomeasure information with other probesets. These methods will be testedusing the PGS and NGS to determine if one is more effective than theothers at removing non expressed genes. The prior odds for a PPinteraction will be estimated approximately at 1 in 800, based onprevious estimates of ˜300,000 PP interactions among 22,000 proteins ina human cell (27, 82; each herein incorporated by reference in itsentirety). From this value, any protein pair, after evidenceintegration, has at least 50% probability of being involved in a PPinteraction. PGS PP interactions will also be included in the HGi.

Protein-DNA Interactions.

A PGS for PD interactions will be generated from the TRANSFACProfessional (61; herein incorporated by reference in its entirety),BIND and Myc (MycDB) databases (110; herein incorporated by reference inits entirety). The NGS will include 100,000 random TF-target pairs,excluding pairs in the PGS interaction or in the same biological processin Gene Ontology. A TF-specific prior odds will be used, since theTF-regulon size is approximated by a power-law distribution (7; hereinincorporated by reference in its entirety). ARACNe inferences (58;herein incorporated by reference in its entirety) will be used toestimate TF-regulon sizes and to compute the TF-specific prior odds. PDinteractions will be inferred from the following evidence sources: (a)mouse interactions from the TRANSFAC Professional and BIND databases;(b) the ARACNe and MINDy algorithms; (c) TF binding site analysis in thepromoter of candidate target genes (85; herein incorporated by referencein its entirety); (d) target gene conditional co-expression based on thegene expression profiles defined in EXAMPLES 2 and 3. PGS interactionswill be included in the HGi.

Post-Translational Modification.

The MINDy algorithm predicts post-translational modulation events, wherea TF and target appear to only have an interaction in the presence orabsence of a third modulator gene (M). These 3-way interactions will besplit into two distinct pairwise interactions: a PD interaction betweenthe TF and its target and a TF-modulator interaction that can be eithera P-TF or a TF-TF interaction, depending on whether the modulator isalso a TF. For the interaction types, one can qualify the accuracy andsensitivity of the Interactome using ROC curves based on 5-fold crossvalidation. Basically, the PGS and NGS will be divided in 5 randomsubsets of equal size. For each subset, one can train the Naïve Bayesclassifier using the remaining four subsets and assess the methodsperformance using the PGS and NGS subsets that were not used fortraining the classifier. The MINDy improvements discussed in EXAMPLES 2and 3 will also be tested to determine the most effective algorithmicapproach.

Use of Alternative Classifiers.

Several successful strategies for evidence integration exist and will beconsidered in alternative to the Naïve Bayes Classifier. These includethe use of voting methods (35; herein incorporated by reference in itsentirety), Bayesian Networks (36; herein incorporated by reference inits entirety), boosting algorithms (9; herein incorporated by referencein its entirety), and Markov Random Fields (17; herein incorporated byreference in its entirety). The latter is interesting in this context asit allows the integration of functional information on existing networkstructures.

The HGi as a Framework for Genetic/Epigenetic/Functional DataIntegration.

As more and more, largely orthogonal data is amassed to inform analysisof tumorigenesis, a key question is how to integrate this data so thateach data modality informs the others. Here, the HGi will be used as anintegrative platform for genetic, epigenetic, and functional datarelated to alterations or dysregulation events in GBM. The simplestlevel of integration will proceed as in Ref. 55 (herein incorporated byreference in its entirety), by determining whether the topologicalneighborhood of each gene is enriched in genetic/epigenetic alterationsor in interactions that are dysregulated within the malignant phenotype.Each gene or gene interaction will be assigned a score based on thedysregulation events that affect it. For instance, if the promoter of agene is found to be differentially methylated in cancer samples, theneach transcriptional interaction upstream of that gene will be assigneda score. Similarly, if a gene copy number alteration affecting a regionthat includes N genes is detected, then each gene will be assigned ascore. Differential mutual information on each interaction in normal vs.malignant samples will also be used to assign a dysregulation score toeach gene-gene interaction (55; herein incorporated by reference in itsentirety).

For each gene, we will then use several enrichment analysis methods,including the Fisher Exact test, GSEA, and others, to assess whether itsneighborhood (i.e. other genes and interactions in its proximity withinthe HGi) is unusually enriched in alterations. As a result, one can planto study methods that propagate dysregulation/alteration information onthe network, which can reduce the dependency on hub size. Since the HGinetwork includes both directed and adirected interactions, use ofindividual approaches such as Bayesian Networks or Markov Random Fieldsis not an option. One can thus explore mixed approaches such asintegrating information on two sub-networks, one fully directed and onefully adirected, at alternate time steps as well as using some recentgraph-theoretic approaches that were specifically designed for this typeof mixed networks. One can define a probability to each gene in thenetwork, that is proportional to the gene's role in tumorigenesis andprogression to high-grade tumors and to integrate information sources tocompute such probability.

Additional Analyses Supported by the HGi.

Availability of the HGi will allow a rich set of interactomes-basedmethodologies to be tested on GBM data. For instance, while thisresearch is specifically aimed at the genetic mechanisms that implementand maintain the most aggressive form of glioma, characterized by amesenchymal signature and phenotype, other important avenues ofinvestigations of the disease are around the dissection of the basicmechanisms of GBM tumorigenesis and the mechanism of action of drugs forthe treatment of GBM. Availability of a complete and unbiased HGi, whichrepresents the full complement of genome-wide molecular interactions inthe disease, will be a significant tool for additional analyses and weexpect that this resource will be heavily used by the community. Forinstance, the IDEA and MRA can be used to dissect normal vs. tumorphenotypes rather than high-grade vs. low-grade glioma as described inthis proposal. Additionally, the approach in EXAMPLES 2-4 and discussedherein can be applied to identify drugs able to implement an apoptoticphenotype in GBM.

LITERATURE CITED FOR EXAMPLES 2-6

-   1. 2008. Comprehensive genomic characterization defines human    glioblastoma genes and core pathways. Nature 455:1061-8.-   2. Aoki, K., G. Meng, K. Suzuki, T. Takashi, Y. Kameoka, K.    Nakahara, R. Ishida, and M. Kasai. 1998. RP58 associates with    condensed chromatin and mediates a sequence-specific transcriptional    repression. J Biol Chem 273:26698-704.-   3. Ashburner, M., C. A. Ball, J. A. Blake, D. Botstein, H.    Butler, J. M. Chemy, A. P. Davis, K. Dolinski, S. S. Dwight, J. T.    Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S.    Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin,    and G. Sherlock. 2000. Gene ontology: tool for the unification of    biology. The Gene Ontology Consortium. Nat. Genet. 25:25-9.-   4. Bader, G. D., D. Betel, and C. W. Hogue. 2003. BIND: the    Biomolecular Interaction Network Database. Nucleic Acids Res    31:248-50.-   5. Bajenaru, M. L., J. Donahoe, T. Corral, K. M. Reilly, S.    Brophy, A. Pellicer, and D. H. Gutmann. 2001. Neurofibromatosis 1    (NF1) heterozygosity results in a cell-autonomous growth advantage    for astrocytes. Glia 33:314-23.-   6. Barabasi, A. L., and Z. N. Oltvai. 2004. Network biology:    understanding the cell's functional organization. Nat Rev Genet.    5:101-13.-   7. Basso, K., A. A. Margolin, G. Stolovitzky, U. Klein, R.    Dalla-Favera, and A. Califano. 2005. Reverse engineering of    regulatory networks in human B cells. Nat Genet. 37:382-90.-   8. Becker, K. G., I. J. Lee, J. W. Nagle, R. D. Canning, A. M.    Gado, R. Torres, M. H. Polymeropoulos, P. T. Massa, W. E. Biddison,    and P. D. Drew. 1997. C2H2-171: a novel human cDNA representing a    developmentally regulated POZ domain/zinc finger protein    preferentially expressed in brain. Int J Dev Neurosci 15:891-9.-   9. Ben-Dor, A., L. Bruhn, N. Friedman, I. Nachman, M. Schummer,    and Z. Yakhini. 2000. Tissue classification with gene expression    profiles. J Comput Biol 7:559-83.-   10. Beurel, E., and R. S. Jope. 2008. Differential regulation of    STAT family members by glycogen synthase kinase-3. J Biol Chem    283:21934-44.-   11. Blits, B., B. M. Kitay, A. Farahvar, C. V. Caperton, W. D.    Dietrich, and M. B. Bunge. 2005. Lentiviral vector-mediated    transduction of neural progenitor cells before implantation into    injured spinal cord and brain to detect their migration, deliver    neurotrophic factors and repair tissue. Restor Neurol Neurosci    23:313-24.-   12. Boyer, L. A., T. I. Lee, M. F. Cole, S. E. Johnstone, S. S.    Levine, J. P. Zucker, M. G. Guenther, R. M. Kumar, H. L.    Murray, R. G. Jenner, D. K. Gifford, D. A. Melton, R. Jaenisch,    and R. A. Young. 2005. Core transcriptional regulatory circuitry in    human embryonic stem cells. Cell 122:947-56.-   13. Bromberg, J. F., M. H. Wrzeszczynska, G. Devgan, Y. Zhao, R. G.    Pestell, C. Albanese, and J. E. Darnell, Jr. 1999. Stat3 as an    oncogene. Cell 98:295-303.-   14. Bussemaker, H. J., H. Ligand E. D. Siggia. 2001. Regulatory    element detection using correlation with expression. Nat Genet.    27:167-71.-   15. Chen, P., A. Iavarone, J. Fick, M. Edwards, M. Prados, and M. A.    Israel. 1995. Constitutional p53 mutations associated with brain    tumors in young adults. Cancer Genet Cytogenet 82:106-15.-   16. Consiglio, A., A. Gritti, D. Dolcetta, A. Follenzi, C.    Bordignon, F. H. Gage, A. L. Vescovi, and L. Naldini. 2004. Robust    in vivo gene transfer into adult mammalian NSCs by lentiviral    vectors. Proc Natl Acad Sci USA 101:14835-40.-   17. Deng, M., K. Zhang, S. Mehta, T. Chen, and F. Sun. 2003.    Prediction of protein function using protein-protein interaction    data. J Comput Biol 10:947-60.-   18. Doetsch, F., I. Caille, D. A. Lim, J. M. Garcia-Verdugo, and A.    Alvarez-Buylla. 1999. Subventricular zone astrocytes are NSCs in the    adult mammalian brain. Cell 97:703-16.-   19. Ehrich, M., M. R. Nelson, P. Stanssens, M. Zabeau, T.    Liloglou, G. Xinarianos, C. R. Cantor, J. K. Field, and D. van den    Boom. 2005. Quantitative high-throughput analysis of DNA methylation    patterns by base-specific cleavage and mass spectrometry. Proc Natl    Acad Sci USA 102:15785-90.-   20. Ergun, A., C. A. Lawrence, M. A. Kohanski, T. A. Brennan,    and J. J. Collins. 2007. A network biology approach to prostate    cancer. Mol Syst Biol 3:82.-   21. Frank, D. A. 2007. STAT3 as a central mediator of neoplastic    cellular transformation. Cancer Lett 251:199-210.-   22. Freije, W. A., F. E. Castro-Vargas, Z. Fang, S. Horvath, T.    Cloughesy, L. M. Liau, P. S. Mischel, and S. F. Nelson. 2004. Gene    expression profiling of gliomas strongly predicts survival. Cancer    Res 64:6503-10.-   23. Fuks, F., W. A. Burgers, N. Godin, M. Kasai, and T.    Kouzarides. 2001. Dnmt3a binds deacetylases and is recruited by a    sequence-specific repressor to silence transcription. Embo J    20:2536-44.-   24. Gasch, A. P., P. T. Spellman, C. M. Kao, O. Carmel-Harel, M. B.    Eisen, G. Storz, D. Botstein, and P. O. Brown. 2000. Genomic    expression programs in the response of yeast cells to environmental    changes. Mol Biol Cell 11:4241-57.-   25. Godard, S., G. Getz, M. Delorenzi, P. Farmer, H. Kobayashi, 1.    Desbaillets, M. Nozaki, A. C. Diserens, M. F. Hamou, P. Y.    Dietrich, L. Regli, R. C. Janzer, P. Bucher, R. Stupp, N. de    Tribolet, E. Domany, and M. E. Hegi. 2003. Classification of human    astrocytic gliomas on the basis of gene expression: a correlated    group of genes with angiogenic activity emerges as a strong    predictor of subtypes. Cancer Res 63:6613-25.-   26. Goh, K. I., M. E. Cusick, D. Valle, B. Childs, M. Vidal,    and A. L. Barabasi. 2007. The human disease network. Proc Natl Acad    Sci USA 104:8685-90.-   27. Hart, G. T., A. K. Ramani, and E. M. Marcotte. 2006. How    complete are current yeast and human protein-interaction networks?    Genome Biol 7:120.-   28. Hart, K. C., S. C. Robertson, and D. J. Donoghue. 2001.    Identification of tyrosine residues in constitutively activated    fibroblast growth factor receptor 3 involved in mitogenesis, Stat    activation, and phosphatidylinositol 3-kinase activation. Mol Biol    Cell 12:931-42.-   29. Hermjakob, H., L. Montecchi-Palazzi, C. Lewington, S. Mudali, S.    Kerrien, S. Orchard, M. Vingron, B. Roechert, P. Roepstorff, A.    Valencia, H. Margalit, J. Armstrong, A. Bairoch, G. Cesareni, D.    Sherman, and R. Apweiler. 2004. IntAct: an open source molecular    interaction database. Nucleic Acids Res 32:D452-5.-   30. Hopkins, A. L., and C. R. Groom. 2002. The druggable genome. Nat    Rev Drug Discov 1:727-30.-   31. Iavarone, A., E. R. King, X. M. Dai, G. Leone, E. R. Stanley,    and A. Lasorella. 2004. Retinoblastoma promotes definitive    erythropoiesis by repressing Id2 in fetal liver macrophages. Nature    432:1040-5.-   32. Iavarone, A., K. K. Matthay, T. M. Steinkirchner, and M. A.    Israel. 1992. Germ-line and somatic p53 gene mutations in multifocal    osteogenic sarcoma. Proc Natl Acad Sci USA 89:4207-9.-   33. Ingenuity Systems, I. www.ingenuity.com.-   34. Inghirami, G., R. Chiarle, W. J. Simmons, R. Piva, K.    Schlessinger, and D. E. Levy. 2005. New and old functions of STAT3:    a pivotal target for individualized treatment of cancer. Cell Cycle    4:1131-3.-   35. Jansen, R., N. Lan, J. Qian, and M. Gerstein. 2002. Integration    of genomic datasets to predict protein complexes in yeast. J Struct    Funct Genomics 2:71-81.-   36. Jansen, R., H. Yu, D. Greenbaum, Y. Kluger, N. J. Krogan, S.    Chung, A. Emili, M. Snyder, J. F. Greenblatt, and M. Gerstein. 2003.    A Bayesian networks approach for predicting protein-protein    interactions from genomic data. Science 302:449-53.-   37. Kalir, S., S. Mangan, and U. Alon. 2005. A coherent feed-forward    loop with a SUM input function prolongs flagella expression in    Escherichia coli. Mol Syst Biol 1:2005 0006.-   38. Kwon, C. H., X. Zhu, J. Zhang, L. L. Knoop, R. Tharp, R. J.    Smeyne, C. G. Eberhart, P. C. Burger, and S. J. Baker. 2001. Pten    regulates neuronal soma size: a mouse model of Lhermitte-Duclos    disease. Nat Genet. 29:404-11.-   39. La Porta, C. A., C. Franchi, and R. Comolli. 1998.    c-PKC-dependent modulation of plasma fibrinogen levels during the    acute-phase response in young and old rats. Mech Ageing Dev    103:317-26.-   40. Lamb, J., E. D. Crawford, D. Peck, J. W. Modell, I. C.    Blat, M. J. Wrobel, J. Lerner, J. P. Brunet, A. Subramanian, K. N.    Ross, M. Reich, H. Hieronymus, G. Wei, S. A. Armstrong, S. J.    Haggarty, P. A. Clemons, R. Wei, S. A. Carr, E. S. Lander, and T. R.    Golub. 2006. The Connectivity Map: using gene-expression signatures    to connect small molecules, genes, and disease. Science 313:1929-35.-   41. Lasorella, A., M. Noseda, M. Beyna, Y. Yokota, and A.    Iavarone. 2000. Id2 is a retinoblastoma protein target and mediates    signalling by Myc oncoproteins. Nature 407:592-8.-   42. Lee, J., S. Kotliarova, Y. Kotliarov, A. L1, Q. Su, N. M.    Donin, S. Pastorino, B. W. Purow, N. Christopher, W. Zhang, J. K.    Park, and H. A. Fine. 2006. Tumor stem cells derived from    glioblastomas cultured in bFGF and EGF more closely mirror the    phenotype and genotype of primary tumors than do serum-cultured cell    lines. Cancer Cell 9:391-403.-   43. Lee, J. P., M. Jeyakumar, R. Gonzalez, H. Takahashi, P. J.    Lee, R. C. Baek, D. Clark, H. Rose, G. Fu, J. Clarke, S.    McKercher, J. Meerloo, F. J. Muller, K. I. Park, T. D.    Butters, R. A. Dwek, P. Schwartz, G. Tong, D. Wenger, S. A.    Lipton, T. N. Seyfried, F. M. Platt, and E. Y. Snyder. 2007. Stem    cells act through multiple mechanisms to benefit mice with    neurodegenerative metabolic disease. Nat Med 13:439-47.-   44. Lee, T. I., N. J. Rinaldi, F. Robert, D. T. Odom, Z.    Bar-Joseph, G. K. Gerber, N. M. Hannett, C. T. Harbison, C. M.    Thompson, I. Simon, J. Zeitlinger, E. G. Jennings, H. L.    Murray, D. B. Gordon, B. Ren, J. J. Wyrick, J. B. Tagne, T. L.    Volkert, E. Fraenkel, D. K. Gifford, and R. A. Young. 2002.    Transcriptional regulatory networks in Saccharomyces cerevisiae.    Science 298:799-804.-   45. Lefebvre, C., W. Lim, K. Basso, R. Dalla Favera, and A.    Califano. 2006. A context-specific network of protein-DNA and    protein-protein interactions reveals new regulatory motifs in human    B cells. RECOMB Satellite Workshop on Systems Biology, San Diego,    December 2006. Also in press in Lecture Notes in Bioinformatics,    Springer Verlag.-   46. Legler, J. M., L. A. Gloeckler Ries, M. A. Smith, J. L.    Warren, E. F. Heineman, R. S. Kaplan, and M. S. Linet. 2000.    RESPONSE: re: brain and other central nervous system cancers: recent    trends in incidence and mortality. J Natl Cancer Inst 92:77 A-8.-   47. Liang, Y., M. Diehn, N. Watson, A. W. Bollen, K. D.    Aldape, M. K. Nicholas, K. R. Lamborn, M. S. Berger, D.    Botstein, P. O. Brown, and M. A. Israel. 2005. Gene expression    profiling reveals molecularly and clinically distinct subtypes of    glioblastoma multiforme. Proc Natl Acad Sci USA 102:5814-9.-   48. Lim, W. K., and A. Califano. 2007. Presented at the RECOMB    Regulatory Genomics, Boston, October 11-13.-   49. Lim, W. K., E. Lyashenko, and A. Califano. 2009. Master    regulators used as breast caqncer metastasis classifier. Pac Symp    Biocomput 14:504-515.-   50. Linding, R., L. J. Jensen, G. J. Ostheimer, M. A. van Vugt, C.    Jorgensen, I. M. Miron, F. Diella, K. Colwill, L. Taylor, K.    Elder, P. Metalnikov, V. Nguyen, A. Pasculescu, J. Jin, J. G.    Park, L. D. Samson, J. R. Woodgett, R. B. Russell, P. Bork, M. B.    Yaffe, and T. Pawson. 2007. Systematic discovery of in vivo    phosphorylation networks. Cell 129:1415-26.-   51. Ludwig, T., P. Fisher, S. Ganesan, and A. Efstratiadis. 2001.    Tumorigenesis in mice carrying a truncating Brcal mutation. Genes    Dev 15:1188-93.-   52. Ludwig, T., P. Fisher, V. Murty, and A. Efstratiadis. 2001.    Development of mammary adenocarcinomas by tissue-specific knockout    of Brca2 in mice. Oncogene 20:3937-48.-   53. Malatesta, P., M. A. Hack, E. Hartfuss, H. Kettenmann, W.    Klinkert, F. Kirchhoff, and M. Gotz. 2003. Neuronal or glial    progeny: regional differences in radial glia fate. Neuron 37:751-64.-   54. Malatesta, P., E. Hartfuss, and M. Gotz. 2000. Isolation of    radial glial cells by fluorescent-activated cell sorting reveals a    neuronal lineage. Development 127:5253-63.-   55. Mani, K. M., C. Lefebvre, K. Wang, W. K. Lim, K. Basso, R. Dalla    Favera, and A. Califano. 2007. A Systems biology approach to    prediction of oncogenes and perturbation targets in B cell    lymphomas. Molecular Systems Biology 4:169-179, 2008.-   56. Margolin, A. A., I. Nemenman, K. Basso, C. Wiggins, G.    Stolovitzky, R. Dalla Favera, and A. Califano. 2006. ARACNE: an    algorithm for the reconstruction of gene regulatory networks in a    mammalian cellular context. BMC Bioinformatics 7 Suppl 1:S7.-   57. Margolin, A. A., T. Palomero, P. Sumazin, A. Califano, A. A.    Ferrando, and G. Stolovitzky. 2009. ChIP-on-chip significance    analysis reveals large-scale binding and regulation by human TF    oncogenes. Proc Natl Acad Sci USA 106:244-9.-   58. Margolin, A. A., K. Wang, W. K. Lim, M. Kustagi, I. Nemenman,    and A. Califano. 2006. Reverse engineering cellular networks. Nat    Protoc 1:662-71.-   59. Marino, S., M. Vooijs, H. van Der Gulden, J. Jonkers, and A.    Berns. 2000. Induction of medulloblastomas in p53-null mutant mice    by somatic inactivation of Rb in the external granular layer cells    of the cerebellum. Genes Dev 14:994-1004.-   60. Matsuo, R., W. Ochiai, K. Nakashima, and T. Taga. 2001. A new    expression cloning strategy for isolation of substrate-specific    kinases by using phosphorylation site-specific antibody. J Immunol    Methods 247:141-51.-   61. Matys, V., E. Fricke, R. Geffers, E. Gossling, M. Haubrock, R.    Hehl, K. Hornischer, D. Karas, A. E. Kel, O. V. Kel-Margoulis, D. U.    Kloos, S. Land, B. Lewicki-Potapov, H. Michael, R. Munch, I.    Reuter, S. Rotert, H. Saxel, M. Scheer, S. Thiele, and E.    Wingender. 2003. TRANSFAC: transcriptional regulation, from patterns    to profiles. Nucleic Acids Res 31:374-8.-   62. Merkle, F. T., A. D. Tramontin, J. M. Garcia-Verdugo, and A.    Alvarez-Buylla. 2004. Radial glia give rise to adult NSCs in the    subventricular zone. Proc Natl Acad Sci USA 101:17528-32.-   63. Mewes, H. W., D. Frishman, K. F. Mayer, M. Munsterkotter, O,    Noubibou, P. Pagel, T. Rattei, M. Oesterheld, A. Ruepp, and V.    Stumpflen. 2006. MIPS: analysis and annotation of proteins from    whole genomes in 2005. Nucleic Acids Res 34:D169-72.-   64. Mulder, N. J., R. Apweiler, T. K. Attwood, A. Bairoch, A.    Bateman, D. Binns, P. Bork, V. Buillard, L. Cerutti, R. Copley, E.    Courcelle, U. Das, L. Daugherty, M. Dibley, R. Finn, W.    Fleischmann, J. Gough, D. Haft, N. Hulo, S. Hunter, D. Kahn, A.    Kanapin, A. Kejariwal, A. Labarga, P. S. Langendijk-Genevaux, D.    Lonsdale, R. Lopez, I. Letunic, M. Madera, J. Maslen, C.    McAnulla, J. McDowall, J. Mistry, A. Mitchell, A. N. Nikolskaya, S.    Orchard, C. Orengo, R. Petryszak, J. D. Selengut, C. J.    Sigrist, P. D. Thomas, F. Valentin, D. Wilson, C. H. Wu, and C.    Yeats. 2007. New developments in the InterPro database. Nucleic    Acids Res 35:D224-8.-   65. Niehof, M., S. Kubicka, L. Zender, M. P. Manns, and C.    Trautwein. 2001. Autoregulation enables different pathways to    control CCAAT/enhancer binding protein beta (C/EBP beta)    transcription. J Mol Biol 309:855-68.-   66. Nigro, J. M., A. Misra, L. Zhang, I. Smirnov, H. Colman, C.    Griffin, N. Ozburn, M. Chen, E. Pan, D. Koul, W. K. Yung, B. G.    Feuerstein, and K. D. Aldape. 2005. Integrated array-comparative    genomic hybridization and expression array profiles identify    clinically relevant molecular subtypes of glioblastoma. Cancer Res    65:1678-86.-   67. Noctor, S. C., A. C. Flint, T. A. Weissman, R. S. Dammerman,    and A. R. Kriegstein. 2001. Neurons derived from radial glial cells    establish radial units in neocortex. Nature 409:714-20.-   68. Odom, D. T., R. D. Dowell, E. S. Jacobsen, L. Nekludova, P. A.    Rolfe, T. W. Danford, D. K. Gifford, E. Fraenkel, G. I. Bell,    and R. A. Young. 2006. Core transcriptional regulatory circuitry in    human hepatocytes. Mol Syst Biol 2:2006 0017.-   69. Orlow, I., A. Iavarone, S. J. Crider-Miller, F. Bonilla, E.    Latres, M. H. Lee, W. L. Gerald, J. Massague, B. E. Weissman, and C.    Cordon-Cardo. 1996. Cyclin-dependent kinase inhibitor p57KIP2 in    soft tissue sarcomas and Wilms'tumors. Cancer Res 56:1219-21.-   70. Palomero, T., W. K. Lim, D. T. Odom, M. L. Sulis, P. J. Real, A.    Margolin, K. C. Barnes, J. O'Neil, D. Neuberg, A. P. Weng, J. C.    Aster, F. Sigaux, J. Soulier, A. T. Look, R. A. Young, A. Califano,    and A. A. Ferrando. 2006. NOTCH1 directly regulates c-MYC and    activates a feed-forward-loop transcriptional network promoting    leukemic cell growth. Proc Natl Acad Sci USA 103:18261-6.-   71. Park, J. 1., C. J. Strock, D. W. Ball, and B. D. Nelkin. 2003.    The Ras/Raf/MEK/extracellular signal-regulated kinase pathway    induces autocrine-paracrine growth inhibition via the leukemia    inhibitory factor/JAK/STAT pathway. Mol Cell Biol 23:543-54.-   72. Park, K. I., M. A. Hack, J. Ourednik, B. Yandava, J. D.    Flax, P. E. Stieg, S. Gullans, F. E. Jensen, R. L. Sidman, V.    Ourednik, and E. Y. Snyder. 2006. Acute injury directs the    migration, proliferation, and differentiation of solid organ stem    cells: evidence from the effect of hypoxia-ischemia in the CNS on    clonal “reporter” NSCs. Exp Neurol 199:156-78.-   73. Park, Y. J., E. S. Park, M. S. Kim, T. Y. Kim, H. S. Lee, S.    Lee, I. S. Jang, M. Shong, D. J. Park, and B. Y. Cho. 2002.    Involvement of the protein kinase C pathway in thyrotropin-induced    STAT3 activation in FRTL-5 thyroid cells. Mol Cell Endocrinol    194:77-84.-   74. Parker, M. A., J. K. Anderson, D. A. Corliss, V. E.    Abraria, R. L. Sidman, K. I. Park, Y. D. Teng, D. A. Cotanche,    and E. Y. Snyder. 2005. Expression profile of an    operationally-defined neural stem cell clone. Exp Neurol 194:320-32:-   75. Pelloski, C. E., A. Mahajan, M. Maor, E. L. Chang, S. Woo, M.    Gilbert, H. Colman, H. Yang, A. Ledoux, H. Blair, S. Passe, R. B.    Jenkins, and K. D. Aldape. 2005. YKL-40 expression is associated    with poorer response to radiation and shorter overall survival in    glioblastoma. Clin Cancer Res 11:3326-34.-   76. Peri, S., J. D. Navarro, R. Amanchy, T. Z. Kristiansen, C. K.    Jonnalagadda, V. Surendranath, V. Niranjan, B. Muthusamy, T. K.    Gandhi, M. Gronborg, N. Ibarrola, N. Deshpande, K. Shanker, H. N.    Shivashankar, B. P. Rashmi, M. A. Ramya, Z. Zhao, K. N.    Chandrika, N. Padma, H. C. Harsha, A. J. Yatish, M. P. Kavitha, M.    Menezes, D. R. Choudhury, S. Suresh, N. Ghosh, R. Saravana, S.    Chandran, S. Krishna, M. Joy, S. K. Anand, V. Madavan, A.    Joseph, G. W. Wong, W. P. Schiemann, S, N. Constantinescu, L.    Huang, R. Khosravi-Far, H. Steen, M. Tewari, S. Ghaffari, G. C.    Blobe, C. V. Dang, J. G. Garcia, J. Pevsner, O. N. Jensen, P.    Roepstorff, K. S. Deshpande, A. M. Chinnaiyan, A. Hamosh, A.    Chakravarti, and A. Pandey. 2003. Development of human protein    reference database as an initial platform for approaching systems    biology in humans. Genome Res 13:2363-71.-   77. Phillips, H. S., S. Kharbanda, R. Chen, W. F. Forrest, R. H.    Soriano, T. D. Wu, A. Misra, J. M. Nigro, H. Colman, L.    Soroceanu, P. M. Williams, Z. Modrusan, B. G. Feuerstein, and K.    Aldape. 2006. Molecular subclasses of high-grade glioma predict    prognosis, delineate a pattern of disease progression, and resemble    stages in neurogenesis. Cancer Cell 9:157-73.-   78. Piccirillo, S. G., B. A. Reynolds, N. Zanetti, G. Lamorte, E.    Binda, G. Broggi, H. Brem, A. Olivi, F. Dimeco, and A. L.    Vescovi. 2006. Bone morphogenetic proteins inhibit the tumorigenic    potential of human brain tumour-initiating cells. Nature 444:761-5.-   79. Ramji, D. P., and P. Foka. 2002. CCAAT/enhancer-binding    proteins: structure, function and regulation. Biochem J 365:561-75.-   80. Rasmussen, S. A., Q. Yang, and J. M. Friedman. 2001. Mortality    in neurofibromatosis 1: an analysis using U.S. death certificates.    Am J Hum Genet. 68:1110-8.-   81. Ridet, J. L., S. K. Malhotra, A. Privat, and F. H. Gage. 1997.    Reactive astrocytes: cellular and molecular cues to biological    function. Trends Neurosci 20:570-7.-   82. Rual, J. F., K. Venkatesan, T. Hao, T. Hirozane-Kishikawa, A.    Dricot, N. L1, G. F. Berriz, F. D. Gibbons, M. Dreze, N.    Ayivi-Guedehoussou, N. Klitgord, C. Simon, M. Boxem, S. Milstein, J.    Rosenberg, D. S. Goldberg, L. V. Zhang, S. L. Wong, G. Franklin, S.    Li, J. S. Albala, J. Lim, C. Fraughton, E. Llamosas, S. Cevik, C.    Bex, P. Lamesch, R. S. Sikorski, J. Vandenhaute, H. Y. Zoghbi, A.    Smolyar, S. Bosak, R. Sequerra, L. Doucette-Stamm, M. E.    Cusick, D. E. Hill, F. P. Roth, and M. Vidal. 2005. Towards a    proteome-scale map of the human protein-protein interaction network.    Nature 437:1173-8.-   83. Rzhetsky, A., I. Iossifov, T. Koike, M. Krauthammer, P. Kra, M.    Morris, H. Yu, P. A. Duboue, W. Weng, W. J. Wilbur, V.    Hatzivassiloglou, and C. Friedman. 2004. Gene Ways: a system for    extracting, analyzing, visualizing, and integrating molecular    pathway data. J Biomed Inform 37:43-53.-   84. Smith, A. D., P. Sumazin, D. Das, and M. Q. Zhang. 2005. Mining    ChIP-chip data for TF and cofactor binding sites. Bioinformatics 21    Suppl 1:1403-12.-   85. Smith, A. D., P. Sumazin, Z. Xuan, and M. Q. Zhang. 2006. DNA    motifs in human and mouse proximal promoters predict tissue-specific    expression. Proc Natl Acad Sci USA 103:6275-80.-   86. Smith, A. D., P. Sumazin, and M. Q. Zhang. 2005. Identifying    tissue-selective TF binding sites in vertebrate promoters. Proc Natl    Acad Sci USA 102:1560-5.-   87. Smith, A. D., P. Sumazin, and M. Q. Zhang. 2007. Tissue-specific    regulatory elements in mammalian promoters. Mol Syst Biol 3:73.-   88. Sosinsky, A., B. Honig, R. S. Mann, and A. Califano. 2007.    Discovering transcriptional regulatory regions in Drosophila by a    nonalignment method for phylogenetic footprinting. Proc Natl Acad    Sci USA 104:6305-10.-   89. Stanssens, P., M. Zabeau, G. Meersseman, G. Remes, Y.    Gansemans, N. Storm, R. Hartmer, C. Honisch, C. P. Rodi, S. Bocker,    and D. van den Boom. 2004. High-throughput MALDI-TOF discovery of    genomic sequence polymorphisms. Genome Res 14:126-33.-   90. Steinman, R. A., A. Wentzel, Y. Lu, C. Stehle, and J. R.    Grandis. 2003. Activation of Stat3 by cell confluence reveals    negative regulation of Stat3 by cdk2. Oncogene 22:3608-15.-   91. Stelzl, U., U. Worm, M. Lalowski, C. Haenig, F. H. Brembeck, H.    Goehler, M. Stroedicke, M. Zenkner, A. Schoenherr, S. Koeppen, J.    Timm, S. Mintzlaff, C. Abraham, N. Bock, S. Kietzmann, A. Goedde, E.    Toksoz, A. Droege, S. Krobitsch, B. Korn, W. Birchmeier, H. Lehrach,    and E. E. Wanker. 2005. A human protein-protein interaction network:    a resource for annotating the proteome. Cell 122:957-68.-   92. Subramanian, A., P. Tamayo, V. K. Mootha, S. Mukherjee, B. L.    Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R.    Golub, E. S. Lander, and J. P. Mesirov. 2005. Gene set enrichment    analysis: a knowledge-based approach for interpreting genome-wide    expression profiles. Proc Natl Acad Sci USA 102:15545-50.-   93. Sun, S., and B. M. Steinberg. 2002. PTEN is a negative regulator    of STAT3 activation in human papillomavirus-infected cells. J Gen    Virol 83:1651-8.-   94. Szulc, J., M. Wiznerowicz, M. O, Sauvain, D. Trono, and P.    Aebischer. 2006. A versatile tool for conditional gene expression    and knockdown. Nat Methods 3:109-16.-   95. Takashima, Y., T. Era, K. Nakao, S. Kondo, M. Kasuga, A. G.    Smith, and S, Nishikawa. 2007. Neuroepithelial cells supply an    initial transient wave of MSC differentiation. Cell 129:1377-88.-   96. Tegner, J., M. K. Yeung, J. Hasty, and J. J. Collins. 2003.    Reverse engineering gene networks: integrating genetic perturbations    with dynamical modeling. Proc Natl Acad Sci USA 100:5944-9.-   97. Tramontin, A. D., J. M. Garcia-Verdugo, D. A. Lim, and A.    Alvarez-Buylla. 2003. Postnatal development of radial glia and the    ventricular zone (VZ): a continuum of the neural stem cell    compartment. Cereb Cortex 13:580-7.-   98. Tso, C. L.; P. Shintaku, J. Chen, Q. Liu, J. Liu, Z. Chen, K.    Yoshimoto, P. S. Mischel, T. F. Cloughesy, L. M. Liau, and S. F.    Nelson. 2006. Primary glioblastomas express mesenchymal stem-like    properties. Mol Cancer Res 4:607-19.-   99. Vescovi, A. L., R. Galli, and B. A. Reynolds. 2006. Brain tumour    stem cells. Nat Rev Cancer 6:425-36.-   100. Wang, K., M. Alvarez, B. Bisikirska, R. Linding, K. Basso, R.    Dalla Favera, and A. Califano. 2009. Dissecting the Interface    Between Signaling and Transcriptional Regulation in Human B Cells.    Pacific Symposium on Biocomputing 14:264-275.-   101. Wang, K., N. Banerjee, A. Margolin, I. Nemenman, and A.    Califano. 2006. Genome-wide discovery of modulators of    transcriptional interactions in human B lymphocytes. Lecture Notes    in Computer Science 3909:348-362.-   102. Wang, K., M. Saito, I. Nemenman, K. Basso, A. A. Margolin, U.    Klein, R. Dalla Favera, and A. Califano. 2008. Genome-wide    identification of transcriptional network modulators in human B    cells. submitted.-   103. Wang, L., T. Kurosaki, and S. J. Corey. 2007. Engagement of the    B-cell antigen receptor activates STAT through Lyn in a    Jak-independent pathway. Oncogene 26:2851-9.-   104. Weinstein, I. B. 2002. Cancer. Addiction to oncogenes—the    Achilles heal of cancer. Science 297:63-4.-   105. Wurmser, A. E., K. Nakashima, R. G. Summers, N. Toni, K. A.    D'Amour, D. C. Lie, and F. H. Gage. 2004. Cell fusion-independent    differentiation of NSCs to the endothelial lineage. Nature    430:350-6.-   106. Yang, J., S. A. Mani, J. L. Donaher, S. Ramaswamy, R. A.    Itzykson, C. Come, P. Savagner, I. Gitelman, A. Richardson,    and R. A. Weinberg. 2004. Twist, a master regulator of    morphogenesis, plays an essential role in tumor metastasis. Cell    117:927-39.-   107. Yin, F., P. L1, M. Zheng, L. Chen, Q. Xu, K. Chen, Y. Y.    Wang, Y. Y. Zhang, and C. Han. 2003. Interleukin-6 family of    cytokines mediates isoproterenol-induced delayed STAT3 activation in    mouse heart. J Biol Chem 278:21070-5.-   108. Yu, H., and M. Gerstein. 2006. Genomic analysis of the    hierarchical structure of regulatory networks. Proc Natl Acad Sci    USA 103:14724-31.-   109. Zardo, G., M. I. Tiirikainen, C. Hong, A. Misra, B. G.    Feuerstein, S. Volik, C. C. Collins, K. R. Lamborn, A. Bollen, D.    Pinkel, D. G. Albertson, and J. F. Costello. 2002. Integrated    genomic and epigenomic analyses pinpoint biallelic gene inactivation    in tumors. Nat Genet. 32:453-8.-   110. Zeller, K. 1., A. G. Jegga, B. J. Aronow, K. A. O'Donnell,    and C. V. Dang. 2003. An integrated database of genes responsive to    the Myc oncogenic TF: identification of direct genomic targets.    Genome Biol 4:R69.-   111. Zhu, X., M. Gerstein, and M. Snyder. 2007. Getting connected:    analysis and principles of biological networks. Genes Dev    21:1010-24.-   112. Zhuo, L., M. Theis, 1. Alvarez-Maya, M. Brenner, K. Willecke,    and A. Messing. 2001. hGFAP-cre transgenic mice for manipulation of    glial and neuronal function in vivo. Genesis 31:85-94.

Example 7 A Transcriptional Module Synergistically Initiates andMaintains Mesenchymal Transformation in the Brain

Using a combination of cellular-network reverse engineering algorithmsand experimental validation assays, a small transcriptional module wasidentified, including six transcription factors (TFs), thatsynergistically regulates the mesenchymal signature of malignant glioma.This is a poorly understood molecular phenotype, never observed innormal neural tissue (A 1-3; each herein incorporated by reference inits entirety). It represents the hallmark of tumor aggressiveness inhigh-grade glioma, and its upstream regulation is so far unknown (A1).Overall, the newly discovered transcriptional module regulates >74% ofthe signature genes, while two of its TFs (Stat3 and C/EBPβ) displayfeatures of initiators and master regulators of mesenchymaltransformation. Ectopic co-expression of Stat3 and C/EBPβ is sufficientto reprogram neural stem cells along the aberrant mesenchymal lineage,while simultaneously suppressing genes associated with the normalneuronal state (pro-neural signature). These effects promote tumorformation in the mouse and endow neural stem cells with the phenotypichallmarks of the mesenchymal state (migration and invasion). Silencingthe two TFs in human high grade glioma-derived stem cells and gliomacell lines leads to the collapse of the mesenchymal signature withcorresponding reduction in tumor aggressiveness. In human tumor samples,combined expression of Stat3 and C/EBPβ correlates with mesenchymaldifferentiation of primary glioma and it is a powerful predictor of poorclinical outcome. Taken together, these results reveal that synergisticactivation of a small transcriptional module, inferred using a systemsbiology approach, is necessary and sufficient to reprogram neural stemcells towards a transformed mesenchymal state. This provides the firstexperimentally validated computational approach to infer mastertranscriptional regulators from signatures of human cancer.

To discover TFs causally linked to the expression of the MGES+ signaturethe conventional paradigm of microarray expression profile based cancerresearch was inverted. Rather than asking which genes are part of theMGES+ signature, a computationally inferred, genomewide transcriptionalinteraction map was interrogated to identify which TFs in the humangenome can induce its overexpression in vivo. Such an unbiased,genome-wide approach was not previously attempted because knowledge ofthe transcriptional regulatory interactions within a specific cellularphenotype is extraordinarily sparse, especially in a mammalian context.Thus, only a handful of candidate TFs can be previously interrogated inthis fashion and only after obtaining large-scale binding and functionalassays in the specific cellular context of interest (A10; hereinincorporated by reference in its entirety). Recently, however, reverseengineering approaches have been pioneered for the genome-wide inferenceof regulatory networks in mammalian cells (A11, A12; herein incorporatedby reference in its entirety) and have been applied to theidentification of lesions associated with the dysregulation oftumor-related pathways (A13; herein incorporated by reference in itsentirety). It has been reasoned that these algorithms can allow one touse causal logic rather than statistical associations (A14, 15; hereinincorporated by reference in its entirety) towards the identification ofmaster regulators of the MGES+ signature. It is shown that theintegration of multiple reverse engineering algorithms, based onexpression profile and sequence data from glioma patients, produceshighaccuracy maps of the regulatory relationships in normal andtransformed neural cells. These computational findings werebiochemically validated and subsequently used to identify thetranscriptional events responsible for initiation and maintenance of themesenchymal phenotype of high-grade glioma.

Specifically, computational, functional, and chromatinimmunoprecipitation (ChIP) experiments motivated by the inferredregulatory network topology point to two TFs (Stat3 and C/EBPβ) asmaster regulators of the mesenchymal signature of human glioma. Ectopicco-expression of the two factors in neural stem cells is sufficient toinitiate expression of the mesenchymal set of genes, suppress proneuralgenes and trigger invasion and a malignant mesenchymal phenotype in themouse. Conversely, silencing of these TFs depletes glioma stem cells andcell lines of mesenchymal attributes and greatly impairs their abilityto invade. Most notably, independent immunohistochemistry experiments in62 human glioma specimens show that concurrent expression of Stat3 andC/EBPβ is significantly associated to the expression of mesenchymalproteins and is an accurate predictor of poorest outcome in gliomapatients.

Methods

ARACNe Network Reconstruction.

ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks),an information-theoretic algorithm for inferring transcriptionalinteractions, was used to identify a repertoire of candidatetranscriptional regulators of the MGES genes. Expression profiles usedin the analysis were previously characterized using Affymetrix HU-133Amicroarrays and preprocessed by MAS 5.0 normalization procedure 1.First, candidate interactions between a TF (x) and its potential target(y) are identified by computing pairwise mutual information, MI[x; y],using a Gaussian kernel estimator (A39) and by thresholding the mutualinformation based on the null-hypothesis of statistical independence(p<0.05 Bonferroni corrected for the number of tested pairs). Then,indirect interactions are removed using the data processing inequality,a well known property of the mutual information. For each TFtarget pair(x, y) we considered a path through any other TF (z) and remove anyinteraction such that MI[x; y]<min(MI[x; z], MI[y; z]).

Stepwise Linear Regression (SLR) Analysis.

A regulatory program for each MGES gene was computed as follows: thelog₂ expression of the i-thMGES gene was considered as the responsevariable and the log₂-expression of the TFs as the explanatory variablesin the linear model log x_(i)=Σα_(ij) log f_(j)+β_(ij) (A24). Here,f_(j) represents the expression of the j-th TF in the model and the(α_(ij), β_(ij)) are linear coupling coefficients computed by standardregression analysis. TFs are iteratively added to the model, by choosingeach time the one producing the smallest relative errorE=Σ|x_(i)−x_(i0)|/x_(i0) between predicted and observed targetexpression. This is repeated until the decrease in relative error is nolonger statistically significant. To avoid excessive multiple hypothesistesting correction, TFs were chosen only among the following: (a) the 55inferred by ARACNe at FDR <0.05 and (b) TFs whose DNA binding signaturewas significantly enriched in the proximal promoter of the MGES genesand that are expressed in the dataset, based on the coefficient ofvariation (CV≧0.5). Then, for each TF, the number of MGES targetprograms it contributed to and the average value of the couplingcoefficient were counted.

Cell Lines and Cell Culture Conditions.

SNB75, SNB19, 293T and Rat1 cell lines were grown in DMEM plus 10% FetalBovine Serum (FBS, Gibco/BRL). GBM-derived BTSCs were grown asneurospheres in NBE media consisting of Neurobasal media (Invitrogen),N2 and B27 supplements (0.5× each; Invitrogen), human recombinant bFGFand EGF (50 ng/ml each; R&D Systems). Murine neural stem cells (mNSCs)(from an early passage of clone C17.2) (A27-29; each herein incorporatedby reference in its entirety) were cultured in DMEM plus 10% FetalBovine Serum (FBS), 5% Horse serum (HS, Gibco/BRL) and 1% L-Glutamine(Gibco/BRL). Subclones are extremely easy to make from this line ofmNSCs. For such stable mNSC subclones, 10% DMEM Tet system Approved(Clontech) was used.

To generate stable mNSC subclones, the cells were transfected withpBigibHLH-B2-FLAG, pcDNA6-V5-C/EBPβ and pBabe-FLAG-Stat3C usingLipofectamine 2000 (Invitrogen), according to the manufacturer'sinstructions. Cells were selected with 3 μg/ml Puromycin (Sigma), 6.5μg/ml Blasticydin (SIGMA), and 300 μg/ml Hygromycin B (Invitrogen).Single clones were isolated and analyzed for the expression of therecombinant proteins using monoclonal antobodies anti-FLAG (M2, SIGMA)and anti-V5 (Invitrogen). bHLH-B2 expression was induced with 2 μg/mlDoxyxycline (Sigma) for 24 hrs. To induce neuronal differentiation,mNSCs were grown in 0.5% Horse serum for 10 days.

Brain tumor stem cells were grown as neurospheres in Neurobasal medium(Invitrogen) containing N2 and B27 supplements and 50 ng/ml of EGF andbasic FGF. Cells were transduced with lentiviruses expressing shRNA forStat3 and C/EBPβ or the empty vector and were analyzed 6 days afterinfection.

Plasmid Constructs.

pcDNA6-V5-C/EBPβ was constructed as follows. cDNA encoding murine C/EBPβwas amplified from pcDNA3.1-mC/EBPβ using the following primers:C/EBPβ-EcoRI-for (5′-GCCTTGGAATTCATGGAAGTGGCCAACTTC-3′; SEQ ID NO: 1)and C/EBPβ-XbaI-rev (5′-GCCTTGTCTAGACGGCAGTGACCGGCCGAGGC-3′; SEQ ID NO:2). The amplified sequence was digested with EcoRI and XbaI andsubcloned into pcDNA6 in frame with V5 tag. To createpBig21-b-HLH-B2-FLAG, pcDNA3.1-bHLHB2-FLAG was digested with EcoRI andsubcloned into pBig21. pBabe-Flag-Stat3C, expressing a constitutiveactive form of murine Stat3.

Chromatin Immunoprecipitation (ChIP).

Chromatin immunoprecipitaion was performed as described in (A40; hereinincorporated by reference in its entirety). SNB75 cells werecross-linked with 1% formaldehyde for 10 min and stopped with 0.125 Mglycine for 5 min. Fixed cells were washed in PBS and harvested insodium dodecyl sulfate buffer. After centrifugation, cells wereresuspended in ice-cold immunoprecipitation buffer and sonicated toobtain fragments of 500-1000 pb. Lysates were centrifuged at full speedand the supernatant was precleared with Protein A/G beads (Santa Cruz)and incubated at 4° C. overnight with 1 μg of polyclonal antibodyspecific for C/EBPβ (sc-150, Santa Cruz), Stat3 (sc-482, Santa Cruz),FosL2 (Fra2, sc-604, Santa Cruz), bHLH-B2 (A300-649A, BETHYLlaboratories), or 1 μg of normal rabbit immunoglobulins (Santa Cruz).The immunocomplexes were recovered by incubating the lysates withprotein A/G for 1 additional hour at 4° C. After washing, theimmunocomplexes were eluted, reverse cross-linked and DNA was recoveredby phenolchloroform extraction and ethanol precipitation. DNA was elutedin 200 μl of water and 1 μl was analyzed by PCR with Platinum Taq(Invitrogen).

A modified protocol was developed for the ChIP assays testinginteraction of TFs with the promoters of mesenchymal genes in primaryGBM samples. Briefly, 30 mg of frozen GBM samples per antibody werechopped into small pieces with a razor blade and transferred in a tubewith 1 ml of culture medium, fixed with 1% formaldehyde for 15 min andstopped with 0.125 M glycine for 5 min. Samples were centrifuged at 4000rpm for 2 min, washed twice and diluted in PBS. Tissues were homogenizedusing a pestel and suspended in 3 ml of ice-cold immunoprecipitationbuffer with protease inhibitors and sonicated. ChIP was then performedas described herein.

Promoter Analysis.

Promoter analysis was performed using the MatInspector software(www.genomatix.de). A sequence of 2 kb upstream and 2 kb downstream fromthe transcription start site was analyzed for the presence of putativebinding sites for each TFs. Primers used to amplify sequencessurroundings the predicted binding sites were designed using the Primer3software (http://frodo.wi.mitedu/cgibin/primer3/primer3_www.cgi; hereinincorporated by reference in its entirety).

Quantitative RT—PCR and Immunohistochemistry.

RNA was prepared with RiboPure kit (Ambion) and subsequently used forfirst strand cDNA synthesis using random primers and SuperScriptllReverse Transcriptase (Invitrogen). Real-time PCR was performed usingiTaq SYBR Green from Biorad. For mNSC subclones, gene expression wasnormalized to GAPDH. For human GBM cell lines and GBM-derived BTSCs 18Sribosomal RNA was used.

Immunohistochemistry was performed as previously described (A41; hereinincorporated by reference in its entirety). Briefly, tumors frompatients with newly diagnosed glioblastoma (none of which were includedin the original microarray analyses) were collected from the archivalcollection of the MD Anderson Pathology department. Following sectioningand deparaffinization, tumor samples were subject to antigen retrievaland incubated overnight at 4° C. with the primary antibody. The primaryantibodies and dilutions were anti-YKL-40 (rabbit polyclonal, Quidel,1:750), anti C/EBPβ, (rabbit polyclonal, Santa Cruz, 1:250) andanti-p-STAT3 (rabbit monoclonal, Cell Signaling 1:25). Scoring forYKL-40 and was based on a 3-tiered system, where 0 was <5% of tumorcells positive, 1 was 5-30% positivity and 2 was >30% of tumor cellspositive. Scores of 1 and 2 were later collapsed into a single value fordisplay purposes on Kaplan-Meier curves. Associations betweenC/EBPβ/Stat3 and YKL-40 were assessed using the Fisher exact test (FET).Associations between C/EBPβ/Stat3 and patients survival were assessedusing the log-rank (Mantel-Cox) test of equality of survivaldistributions.

Microarray Analysis.

RNA was prepared with RiboPure kit (Ambion) and assessed for qualitywith an Agilent 2100 Bioanalyzer. Cy₃ labeled cRNA was prepared withAgilent low RNA input linear amplification kit according to themanufacturer's instructions, and hybridized to an Agilent 8×15Kone-color customized array. The array was designed with E-array software4.0 (Agilent, Palo Alto, Calif.) and included 14,851 probe setscorresponding to 2,945 mouse and 3,363 human genes. For the analysis,each array was normalized to its 75% quantile so that gene expressionprofiles can be compared across samples.

Gene Set Enrichment Analysis (GSEA).

To test whether specific gene signatures were globally differentiallyregulated, we used the Gene Set Enrichment analysis method (A31; hereinincorporated by reference in its entirety). In this method, theKolmogorov-Smirnoff test is used to determine whether two gene lists arestatistically correlated. In this case, one list includes genes on themicroarray expression profile dataset, ranked by their differentialexpression statistics across two conditions (e.g. ectopically expressedStat3C/C/EBPβ vs. control), from most over- to most underexpressed. Theother list contains non-ranked genes in a specific signature (e.g.mesenchymal). This is very useful to detect, for instance, situationswhere signature genes can be differentially expressed as a whole, eventhough the fold-change can be small for each gene in isolation. In thiscase, a gene-by-gene test, such as a T-test, cannot reveal statisticalsignificance. The algorithm was set to implement weighted scoring schemeand the enrichment score significance was assessed by 1000 permutationtests.

Migration and Invasion Assays.

For the wound assay testing migration, mNSCs were plated in 60 mm dishesand grown until 95% confluence. To initiate the experiment, a scratch ofapproximately 400 μm was made with a P1000 pipet tip and images weretaken every 24 h over the course of 4 days with an inverted microscope.In the PDGF experiment, the cells were incubated for 24 h with 20 μg/mlPDGF-BB (R&D systems) before making the scratch.

For the Matrigel invasion assay, mNSCs (1×10⁴) were added to the top ofthe chamber of a 24 well BioCoat Matrigel Invasion Chambers (BD) in 500μl volume of serum free DMEM. The lower compartment of the chamber wasfilled with DMEM containing either 0.5% horse serum or 20 μg/ml PDGF-BB(R&D systems) as chemoattractants. After incubation for 24 h, invadingcells were fixed, stained and counted according to the manufacturer'sinstructions. For SNB19 transduced with shRNA expressing lentivirus,1.5×10⁴ cells were plated in the top of the chamber. The lowercompartment contained 5% FBS.

Lentivirus Production and Infection.

Lentiviral expression vectors carrying shRNAs (short hairpin RNAs)specific for C/EBPβ and Stat3 were purchased from Sigma and virus stockswere prepared as recommended by the supplier. The C/EBPβ specific shRNA(shC/EBPβ) has the following sequence: 5′-CCGGCATCGACTACAAACGGAACTTCTCGAGAAGTTCCGTTTGTAGTCGATGTTTTTG-3′ (SEQ ID NO: 3). The Stat3-specificshRNA (shStat3) has the following sequence:5′-CCGGCCTGAGTTGAATTATCAGCTTCT CGAGAAGCTGATAATTCAACTCAGGTTTTTG-3′ (SEQID NO: 4). To generate lentiviral particles, the lentiviral plasmidswere co-transfected along with helper plasmids into human embryonickidney 293T cells. Each shRNA expression plasmid (5 μg) was mixed withpCMVdR8.91 (2.5 μg) and pCMV-MD2.G (1 μg) vectors and transfected intohuman embryonic kidney 293T cells using the Fugene 6 reagent (Roche).Media from these cultures were collected after 24 h, centrifuged 10 minat 2500 rpm, passed through a 0.45-μm filter and used as source forlentiviral shRNAs. A second virus collection was performed 48 h aftertransfection.

To knockdown Stat3 and C/EBPβ, SNB19 (1×10⁵) were plated in 6 wellculture plates and incubated for 24 h. Cells were transduced with Stat3and C/EBPβ sh-RNA or non target control shRNA lentiviral particles.After overnight incubation, fresh culture media were exchanged, and thetransduced cells were cultured in a CO₂ incubator for 5 days.

To infect GBM-derived BTSCs, lentiviral stocks were prepared as follows.Briefly, 293T cells were transfected as before with shRNA expressionplasmids or non target control and supernatant collected after 24 h,centrifuged 10 min at 2500 rpm and passed through a 0.45-μm filter. Thelentiviral particles were then ultracentrifuged for 1.5 h at 25,000 rpmwith a SW28 rotor and diluted in 100 μl PBS1% BSA. The lentiviral titerwas determined after transfection of Rat1 cells with serial dilution ofthe virus. GBM-derived BTSCs were plated as neurospheres in 24 wellplates at 1×10⁴ cells/well and infected with shRNA expressing lentiviralstock at a multiplicity of infection (MOI) of 25. After 6 h 500 μl offresh neurobasal medium was added. Cells were harvested after 5 days andsubjected to gene expression analysis by qRT-PCR and microarray geneexpression profiles.

Tumor Growth in Nude Mice and Immunohistochemistry.

6 weeks BALBc/nude mice were injected subcutaneously with C17.2 neuralstem cell transduced with empty vector (bottom flank, left) orexpressing Stat3C plus C/EBPβ (bottom flank, right). Four mice wereinjected with 2.5×10⁶ and four mice were injected with 5×10⁶ cells in200 μA PBS/Matrigel. Mice were sacrificed after 10 (5×10⁶) or 13 weeks(2.5×10⁶) after the injection. Tumors were removed, fixed in formalinovernight and processed for the analysis of tumor histology andimmunohistochemistry. Tumor sections were subjected todeparaffinization, followed by antigen retrieval and incubated overnightat 4 degrees (Nestin, CD31, FGFR-1 and OSMR) or 1 h at room temperature(Ki67) with the primary antibody. Primary antibodies and dilutions wereNestin (mouse monoclonal, BD, 1:150), CD31, (mouse monoclonal, BD,1:100), Ki67 (rabbit polyclonal, Novocastra laboratories, 1:1000), FGFR1(rabbit polyclonal, Abgent, 1:100), and OSMR (goat polyclonal, R&D,1:50).

Results

Computational Identification of the Transcriptional Regulation ModuleDriving the Mesenchymal Signature of High-Grade Glioma.

ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks)was used to compute a comprehensive, genome-wide repertoire ofregulatory interactions between any TF and the 102 genes in the MGES+signature of high grade glioma. TFs were identified based on their GeneOntology annotation (A16; herein incorporated by reference in itsentirety) and only genes represented in the microarray expressionprofile data were considered in the analysis.

ARACNe is an information theoretic approach for the reverse engineeringof transcriptional interactions from large sets of microarray expressionprofiles. This algorithm was able to identify validated targets of theMYC and NOTCH1 TFs in B and T cells (A11, A17; each herein incorporatedby reference in its entirety). Here ARACNe was adapted towards a farmore challenging goal, namely the unbiased identification of TFsassociated with a given gene expression signature (MGES of human highgrade glioma). The dataset used in this analysis included 176 grade III(anaplastic astocytoma) and grade IV (glioblastoma multiforme, GBM)samples (A1, A18, A19; each herein incorporated by reference in itsentirety). These samples were previously classified into three molecularsignature groups—proneural, proliferative, and mesenchymal—based on theidentification of coordinated expression of specific gene sets byunsupervised cluster analysis1.

The Fisher Exact Test (FET) was then used to determine whether theARACNe inferred targets of a TF overlaps with the MGES genes in astatistically significant way, thus indicating specificity in theregulation of the MGES+. From a list of 1018 TFs, a subset of 55 MGES+specific regulators was inferred, at a false discovery rate (FDR)smaller than 5%. This suggests that relatively few TFs synergisticallycontrol the MGES+ signature, as indicated from a combinatorial,scale-free regulation model (hubs). Remarkably, the six moststatistically significant TFs emerging from this analysis (Stat3,C/EBPβ/δ, bHLH-B2, Runx1, FosL2, and ZNF238) collectively control >74%of the MGES genes (FIG. 1). Clearly, this is a lower bound becauseARACNe has a very low false positive rate but a relatively high falsenegative rate. Thus, many targets will be missed by the analysis.

Consistent with their previously reported activity (A20, A21; eachherein incorporated by reference in its entirety), correlation analysisreveals that five are activators (Stat3, C/EBPβ/δ, bHLH-B2, Runx1, andFosL2) and one is a repressor (ZNF238) of the MGES+ genes. This canfurther indicate their potential as oncogenes or tumor suppressors,respectively. Since both C/EBPβ and C/EBPδ were among the top TF hubsand are known to form stoichiometric homo and heterodimers withidentical DNA binding specificity and redundant transcriptional activity(A22), the term C/EBP is used generically to indicate the TF complex.The interactions inferred for each TF show statistically significantoverlap, indicating that the six TFs are involved in combinatorialregulation of the MGES targets. This biochemically validated findingsuggests a hierarchical, combinatorial control mechanism that providesboth redundancy and fine-grain control of the mesenchymal signature ofbrain tumor cells by a handful of TFs.

Computational Validation of the Mesenchymal TFs Network as Regulator ofthe MGES.

A stepwise linear regression (SLR) method was then used to infer asimple quantitative transcriptional regulation model (i.e. a regulatoryprogram) for the MGES+ genes. In this model, the log-expression of eachtarget gene is approximated by a linear combination of thelog-expression of a small set of TFs using linear regression (A23, A24;each herein incorporated by reference in its entirety). This allows aconvenient linear representation of multiplicative interactions betweenTF activities (combinatorial regulation). TFs are added one at the timeto the model, by choosing the one that produces the greatest reductionin the relative error on the predicted vs. observed expression, untilthe reduction is no longer statistically significant. TFs were thenlooked for that were used to model the largest number of MGES genes (seeMethods). The top six TFs inferred by the FET analysis on ARACNe targetswere also among the top eight inferred by SLR. Among them, the three TFswith the highest average value of their linear coupling coefficient wereC/EBP (α=0.42), bHLH-B2 (α=0.41), and Stat3 (α=0.40), indicating theirpotential role as master regulators of the MGES genes with the nextstrongest TF, ZNF238, showing a negative coefficient (α=−0.34).

Biochemical Validation of TF Binding Sites.

To further validate the inferred MGES regulation network, each TF wastested for its ability to bind to the promoter region (proximalregulatory DNA) of its predicted mesenchymal targets. The targetpromoters were first analyzed in silico to identify putative bindingsites (see Methods). ChIP assays were then performed near predictedsites in the human glioma cell line SNB75 to validate targets of Stat3,bHLH-B2, C/EBPβ and FosL2, for which appropriate reagents wereavailable. On average, about 80% of the tested genomic regions wereimmunoprecipitated by specific antibodies for these TFs but not controlantibodies (FIG. 3). Given that binding can occur via co-factor oroutside of the selected region, this provides a conservative lowerboundof the number of actual mesenchymal targets bound by these TFs. One canconclude that ARACNe accurately recapitulates the transcriptionalactivity of Stat3, bHLH-B2, C/EBPβ and FosL2 on the MGES genes inmalignant glioma.

Mesenchymal TFs from Malignant Glioma Form a Highly Connected andHierarchically Organized Module.

ChIP assays revealed that Stat3 and C/EBP occupy their own promoter andare thus involved in autoregulatory (AR) loops (FIG. 4A, 4B).Additionally, Stat3 occupies the FosL2 and Runx1 promoters; C/EBPβoccupies those of Stat3, FosL2, bHLH-B2, C/EBPβ, and C/EBPδ (the lattertwo confirm the redundant autoregulatory activity of the two C/EBPsubunits, FIG. 3 b) (A22, A25; each herein incorporated by reference inits entirety); FosL2 occupies those of Runx1 and bHLH-B2 (FIG. 4C);finally bHLH-B2 occupies only that of Runx1 (FIG. 4D). The MGES+ controltopology that emerges from this promoter occupancy analysis isremarkably modular (high number of intra-module interactions) anddisplays a clearly hierarchical structure (FIG. 4E). At the very top ofthis hierarchical control structure, we find Stat3 and C/EBP, which arealso involved in AR and form feed-forward (FF) loops with a largefraction of the MGES genes. FF loops involving only positive regulationhave been shown to filter short input transient signals and thus helpmake such a network topology less sensitive to short, randomfluctuations (A26; herein incorporated by reference in its entirety).Whether the interactions between these two TFs and the promoters oftheir mesenchymal targets is conserved in tumor tissues was then tested.Experimental conditions were developed to perform Stat3 and C/EBPβ ChIPassays in two human GBM samples in the mesenchymal signature group. Theexperiments confirmed that, also in this in vivo context, Stat3 andC/EBPβ bind to the MGES targets predicted by the computationalalgorithms (FIGS. 16A-16B). Taken together, these findings suggest thatthe six inferred TFs form a hierarchical regulatory module and thatStat3 and C/EBP can operate as master regulators of the mesenchymalsignature of malignant gliomas.

Combined Expression of C/EBPβ and Stat3 Prevents NeuronalDifferentiation and Reprograms Neural Stem Cells Towards the MesenchymalLineage.

Without being bound by theory, Neural stem cells (NSCs) are the cell oforigin for malignant gliomas in the mesenchymal subgroup (A1; hereinincorporated by reference in its entirety). However, whether mesenchymaltransformation in glial tumors recapitulates a normal albeit rare cellfate determination event intrinsic to NSCs remains unknown (A2, A3, A9;each herein incorporated by reference in its entirety). Whether combinedexpression of Stat3 and C/EBPβ in NSCs is sufficient to initiatemesenchymal gene expression and to trigger the mesenchymal propertiesthat characterize high-grade gliomas was next considered. To do this, anearly passage of the stable, clonal population of mouse NSCs known asC17.2 was used. The enhanced, yet constitutively self-regulatedexpression of sternness genes permits these cells to be efficientlygrown as undifferentiated monolayers in sufficiently large, homogeneousand viable quantities to ensure reproducible patterns of self-renewaland differentiation without ever behaving in a tumorigenic fashion invitro or in vivo (A27-29; each herein incorporated by reference in itsentirety).

Following ectopic expression of C/EBPβ and a constitutively active formof Stat3 (Stat3C) (A30; herein incorporated by reference in itsentirety) in NSCs, we observed dramatic morphologic changes, consistentwith loss of ability to differentiate along the neuronal lineage (FIG.5A). Parental and vectortransfected NSCs have the classicalspindle-shaped morphology that is associated with the neuralstem/progenitor cell phenotype. When grown in the absence of mitogens,these cells display efficient neuronal differentiation characterized byformation of a neuritic network (FIG. 5A, top-right panel). Conversely,expression of C/EBPβ and Stat3C leads to cellular flattening andmanifestation of a fibroblast-like morphology. Remarkably, depletion ofmitogens resulted in additional flattening with complete loss of everyneuronal trait (FIG. 5A, bottom-right panel). These results indicatethat expression of C/EBPβ and Stat3C efficiently suppressesdifferentiation along the neuronal lineage and induces establishedmesenchymal features.

Next, whether C/EBPβ and Stat3C induce expression of the MGES+ genes invivo was considered. To do this, mRNA was extracted from duplicatesamples of two independent C/EBPβ/Stat3C expressing and control clonesof NSCs and hybridized custom expression arrays (Agilent Technologies),containing probes for 6,308 glioma-specific mouse and human genes. TheGene Set Enrichment Analysis method (GSEA, (A31; herein incorporated byreference in its entirety)) was used to test the enrichment of themesenchymal, proliferative and'proneural signatures (A1; hereinincorporated by reference in its entirety) among differentiallyexpressed genes in C/EBPβ/Stat3C expressing versus control cells. Thealgorithm was set to implement weighted scoring scheme and theenrichment score significance is assessed by 1,000 permutation tests tocompute the enrichment p-value. The analysis demonstrated that theglobal mesenchymal and proliferative signatures are both highly enrichedin genes that are overexpressed in C/EBPβ/Stat3C-expressing NSCs.Conversely, the proneural signature is enriched in genes that areunderexpressed in these cells (FIG. 5B). Consistent with these findings,several mesenchymal-specific gene categories are highly enriched inC/EBPβ/Stat3C expressing NSCs.

Quantitative RT-PCR (qRT-PCR) of the microarray results was alsovalidated for a subset of Stat3 and C/EBPβ targets. Interestingly, thegenes coding for the receptors of the growth factors PDGF, EGF and bFGFwere among the most upregulated genes in NSCs expressing Stat3C andC/EBPβ. Outputs from these growth factors provide essential signals forproliferation and invasion of glial tumor cells and are able to revertmature neural cells into pluripotent stem-like cells, an effect that cancontribute to the mesenchymal transformation of NSCs (A32, A33; eachherein incorporated by reference in its entirety). Other genes markedlyoverexpressed in C/EBPβ/Stat3C expressing NSCs are those coding for themorphogenetic proteins BMP4 and BMP6, two crucial inducers of tumorinvasion and angiogenesis (A34, A35; each herein incorporated byreference in its entirety). Thus, Stat3 and C/EBPβ are sufficient toinduce reprogramming of neuralstem cells towards an aberrant mesenchymallineage.

Neural Stem Cells Expressing Stat3 and C/EBPβ Acquire the Hallmarks ofMesenchymal Aggressiveness and Tumorigenic Capability In Vitro and InVivo.

Whether activation of the MGES by Stat3 and C/EBPβ is sufficient totransform NSCs into cells that can efficiently migrate and invade, twoproperties invariably associated with MGES+ in high grade glioma (A1,A2; each herein incorporated by reference in its entirety) wasconsidered. The first assay used to address this question (“woundassay”) evaluates the ability to migrate and fill a scratch introducedin cultures of adherent cells (FIG. 5C). The second (“Matrigel invasionassay”) tests how cells invade a Boyden chamber filter coated with aphysiologic mixture of extracellular matrix components and concentratethe lower side of the filter (FIG. 5D). When the two assays wereperformed on C/EBPβ/Stat3C-expressing and control NSCs clones, we foundthat the expression of the two TFs robustly promoted migration andinvasion through the extracellular matrix (FIGS. 5C-5D). The effects ofC/EBPβ and Stat3C on migration and invasion of NSCs were similar in theabsence of mitogens or in the presence of PDGF (FIG. 5D). Conversely,ectopic bHLHB2 was irrelevant for the MGES and phenotypic behavior ofStat3C-C/EBPβ-expressing NSCs.

To ask whether Stat3 and C/EBPβ confer tumorigenic potential to neuralstem cells in vivo, sub-cutaneous heterotopic transplantation ofC17.2-Stat3C/C/EBPβ (and empty vector as control) was used. Male,six-week old BALB/nude mice (a total of eight animals in two separateexperiments) were injected subcutaneously with 2.5×10⁶ and 5×10⁶C17.2-Stat3C/C/EBPβ cells (right flank) or C17.2-Vector (left flank) inPBS-Matrigel. C17.2-Stat3C/C/EBPβ cells developed fast-growing tumorswith high efficiency (4 out of 4 mice in the group injected with 5×10⁶cells and 3 out of 4 mice in the group injected with 2.5×10⁶ cells),whereas neural stem cells transduced with empty vector never formedtumors (FIG. 6A). Histological analysis demonstrated that the tumorsresembled human high grade glioma, exhibited large areas of polymorphiccells, had tendency to form pseudopalisades with central necrosis andalthough injected in the flank, a low angiogenic site, displayedvascular proliferation, as confirmed by immunostaining for theendothelial marker CD31 (FIGS. 6B-6C). Proliferation in the tumors wasvery high as determined by reactivity for Ki67. In line with thepresence of stem-like cells, human GBM regularly exhibit expression ofprimitive markers. Corroborating this, it was found that the tumorsstained positive for the progenitor marker nestin (FIG. 6C). Finally,positive immunostaining for the mesenchymal signature proteins OSMR andthe FGF receptor-1 (FGFR-1) indicated that oncogenic transformation ofneural stem cells had occurred in the context of reprogramming towardsthe mesenchymal lineage (FIG. 6D). Together, these findings establishthat introduction of the two master regulators of MGES in NSCs not onlyinduces expression of the entire mesenchymal signature but is alsosufficient to transduce to these cells the key phenotypiccharacteristics of glioma aggressiveness that have been previouslyassociated with the signature.

Stat3 and C/EBPβ are Essential for Expression of the MGES andAggressiveness of Human Glioma Cells and Primary Tumors.

To assess the significance of constitutive Stat3 and C/EBPβ in theglioma cells responsible for tumor growth in humans, it was sought toabolish the expression of Stat3 and C/EBPβ in GBM-derived brain tumorstem-like cells that closely mimic the genotype, gene expression andbiology of their parental primary tumors (GBM-BTSCs) (A36; hereinincorporated by reference in its entirety). Transduction of GBMBTSCswith specific shRNA-carrying lentiviruses efficiently silencedendogenous Stat3 and C/EBPβ (FIG. 7A). Gene expression profile analysisusing GSEA showed that depletion of Stat3 and C/EBPβ in GBM-BTSCsdramatically suppressed expression of the MGES genes (FIGS. 7B-7C). Lossof Stat3 and C/EBPβ from GBM-BTSCs led to marked down-regulation of theexpression of the second layer of TFs (bHLH-B2, FosL2, Runx1) associatedwith the glioma derived MGES (FIG. 4F). This finding validates thehierarchical nature of the mesenchymal TFs subnetwork that emerged fromChIP (FIG. 7D).

Next, the human glioma cell line SNB19 (that clusters with tumors of themesenchymal group) was infected with the shStat3 and shC/EBPβlentiviruses and confirmed that silencing of Stat3 and C/EBPβ depletedthe mesenchymal signature even in established glioma cell lines (FIG.7D). Furthermore, silencing of the two master TFs of MGES in SNB19 cellseliminated 80% of their ability to invade through Matrigel (FIG. 7E). Asfinal test for the mesenchymal regulatory role of Stat3 and C/EBPβ inhuman glioma, immunohistochemical analysis for C/EBPβ and active,phospho-Stat3 was conducted in human tumor specimens, and expression ofthese TFs was compared with YKL-40 (a well-established mesenchymalprotein also known as CHI3L1) (A19, A37; herein incorporated byreference in its entirety) as well as patient outcome in a collection of62 newly diagnosed GBMs. FET showed that expression of either C/EBPβ andStat3 were significantly correlated with YKL-40 expression (C/EBPβ,p=4.9×10⁻⁵; Stat3, p=2.2×10⁻⁴). However, the correlation was higher whendouble positive tumors (C/EBPβ+/Stat3+) were compared to doublenegatives (C/EBPβ−/Stat3−, p=2.7×10⁻⁶). Furthermore, double positivetumors were associated with markedly worse clinical outcome than tumorsthat were either single or double negatives (log-rank test, p=0.0002,FIG. 7F). Positivity for either of the two TFs remained predictive ofnegative outcome but with lower statistical strength than doublepositivity (C/EBPβ, p=0.0022; Stat3, p=0.0017). These results providecompelling indication that the synergistic activation of C/EBPβ andStat3 generates mesenchymal properties and marks the worst survivalgroup of GBM patients.

Discussion

It has been shown that expression of Stat3 and C/EBPβ is necessary andsufficient to initiate and maintain the mesenchymal signature ofhigh-grade glioma in neural cells. Remarkably, these two genes wereidentified in a completely unbiased and genome-wide fashion by acomputational systems biology approach. In this context, the traditionalparadigm of gene expression profile based cancer research, yielding longlists of differentially expressed genes (i.e., cancer signatures),becomes just a starting point for a more detailed and rationalcellular-network based analysis where the regulators of thedifferentially expressed signature are identified using a causal model,reflecting physical TF-DNA interactions, rather than statisticalassociations. This yields a repertoire of candidate transcriptionalinteractions that can be further interrogated using both computationaland experimental techniques to determine topology, modularity, andmaster regulation properties. Further computational and experimentalanalysis revealed that among candidate TFs, Stat3 and C/EBPβ not onlydirectly regulate their own set of transcriptional mesenchymal targetsbut also participate in the hierarchical regulation of several otherTFs, which were in turn validated as regulators of the MGES genes.

Taken together, these results indicate that the co-expression of C/EBPβand constitutively active Stat3 convert neural stem cells towards amesenchymal lineage fate with coordinated induction of a MGES+.Consistently, C/EBPβ/Stat3C-expressing neural stem cells lose theirability to differentiate along the neuronal lineage and express thenormal proneural signature genes. Such a finding reflects the mutuallyexclusive expression of the proneural and mesenchymal signaturesobserved in primary GBM (A1; herein incorporated by reference in itsentirety) and is further indication that C/EBPβ and Stat3C are masterregulator genes, capable of inducing the mesenchymal signature ofhigh-grade glioma in neural stem cells. Without being bound by theory,the neuroepithelial to mesenchymal reprogramming induced by Stat3 andC/EBPβ TFs in neural stem cells recapitulates the epithelial tomesenchymal transition frequently described in epithelial neoplasmsundergoing progression towards a more invasive and metastatic tumor type(A38; herein incorporated by reference in its entirety). Thus, anexciting implication of this work is that, by acting upstream of themesenchymal genes, C/EBP/Stat3-mediated transcription reprograms thecell fate of neural stem cells towards an aberrant “mesenchymal”lineage. This transformation triggers the most aggressive properties ofmalignant brain tumors, namely invasion and neo-angiogenesis. Sinceexpression of Stat3 and C/EBPβ is essential to maintain the mesenchymalproperties of human glioma cells, they provide important clues fordiagnostic and pharmacological intervention. Immunohistochemistry assaysin independent GBM samples confirmed that, based on the correlation withYKL-40, Stat3 and C/EBPβ are strongly linked to the mesenchymal stateand their combined expression provides an excellent prognostic biomarkerfor tumor aggressiveness.

In conclusion, the studies present the first evidence that computationalsystems biology methods can be effectively used to infer masterregulator genes that choreograph the malignant transformation of a humancell. This is a general new paradigm that will be applicable to thedissection of any normal and pathologic phenotypic state.

REFERENCES

-   A1. Phillips, H. S. et al. Molecular subclasses of high-grade glioma    predict prognosis, delineate a pattern of disease progression, and    resemble stages in neurogenesis. Cancer Cell 9, 157-173 (2006).-   A2. Tso, C. L. et al. Primary glioblastomas express mesenchymal    stem-like properties. Mol Cancer Res 4, 607-619 (2006).-   A3. Wurmser, A. E. et al. Cell fusion-independent differentiation of    neural stem cells to the endothelial lineage. Nature 430, 350-356    (2004).-   A4. Ohgaki, H. & Kleihues, P. Population-based studies on incidence,    survival rates, and genetic alterations in astrocytic and    oligodendroglial gliomas. J Neuropathol Exp Neurol 64, 479-489    (2005).-   A5. Demuth, T. & Berens, M. E. Molecular mechanisms of glioma cell    migration and invasion. J Neurooncol 70, 217-228 (2004).-   A6. Kargiotis, O., Rao, J. S. & Kyritsis, A. P. Mechanisms of    angiogenesis in gliomas. J Neurooncol 78, 281-293 (2006).-   A7. Hoelzinger, D. B., Demuth, T. & Berens, M. E. Autocrine factors    that sustain glioma invasion and paracrine biology in the brain    microenvironment. J Natl Cancer Inst 99, 1583-1593 (2007).-   A8. Visted, T., Enger, P. O., Lund-Johansen, M. & Bjerkvig, R.    Mechanisms of tumor cell invasion and angiogenesis in the central    nervous system. Front Biosci 8, e289-304 (2003).-   A9. Takashima, Y. et al. Neuroepithelial cells supply an initial    transient wave of MSC differentiation. Cell 129, 1377-1388 (2007).-   A10. Cheng, A. S. et al. Combinatorial analysis of transcription    factor partners reveals recruitment of c-MYC to estrogen    receptor-alpha responsive promoters. Mol Cell 21, 393-404 (2006).-   A11. Basso, K. et al. Reverse engineering of regulatory networks in    human B cells. Nat Genet. 37, 382-390 (2005).-   A12. Margolin, A. A. et al. ARACNE: an algorithm for the    reconstruction of gene regulatory networks in a mammalian cellular    context. BMC Bioinformatics 7 Suppl 1, S7 (2006).-   A13. Mani, K. M. et al. A Systems biology approach to prediction of    oncogenes and perturbation targets in B cell lymphomas. Molecular    Systems Biology in press (2007).-   A14. Hanauer, D. A., Rhodes, D. R., Sinha-Kumar, C. &    Chinnaiyan, A. M. Bioinformatics approaches in the study of cancer.    Curr Mol Med 7, 133-141 (2007).-   A15. Lander, A. D. A calculus of purpose. PLoS Biol 2, e164 (2004).-   A16. Ashburner, M. et al. Gene ontology: tool for the unification of    biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29 (2000).-   A17. Palomero, T. et al. NOTCH1 directly regulates c-MYC and    activates a feed-forward-loop transcriptional network promoting    leukemic cell growth. Proc Natl Acad Sci USA 103, 18261-18266    (2006).-   A18. Freije, W. A. et al. Gene expression profiling of gliomas    strongly predicts survival. Cancer Res 64, 6503-6510 (2004).-   A19. Nigro, J. M. et al. Integrated array-comparative genomic    hybridization and expression array profiles identify clinically    relevant molecular subtypes of glioblastoma. Cancer Res 65,    1678-1686 (2005).-   A20. Aoki, K. et al. RP58 associates with condensed chromatin and    mediates a sequence-specific transcriptional repression. J Biol Chem    273, 26698-26704 (1998).-   A21. Fuks, F., Burgers, W. A., Godin, N., Kasai, M. & Kouzarides, T.    Dnmt3a binds deacetylases and is recruited by a sequence-specific    repressor to silence transcription. Embo J 20, 2536-2544 (2001).-   A22. Ramji, D. P. & Foka, P. CCAAT/enhancer-binding proteins:    structure, function and regulation. Biochem J 365, 561-575 (2002).-   A23. Bussemaker, H. J., Li, H. & Siggia, E. D. Regulatory element    detection using correlation with expression. Nat Genet. 27, 167-171    (2001).-   A24. Tegner, J., Yeung, M. K., Hasty, J. & Collins, J. J. Reverse    engineering gene networks: integrating genetic perturbations with    dynamical modeling. Proc Natl Acad Sci USA 100, 5944-5949 (2003).-   A25. Niehof, M., Kubicka, S., Zender, L., Manns, M. P. &    Trautwein, C. Autoregulation enables different pathways to control    CCAAT/enhancer binding protein beta (C/EBP beta) transcription. J    Mol Biol 309, 855-868 (2001).-   A26. Kalir, S., Mangan, S. & Alon, U. A coherent feed-forward loop    with a SUM input function prolongs flagella expression in    Escherichia coli. Mol Syst Biol 1, 2005 0006 (2005).-   A27. Lee, J. P. et al. Stem cells act through multiple mechanisms to    benefit mice with neurodegenerative metabolic disease. Nat Med 13,    439-447 (2007).-   A28. Park, K. I. et al. Acute injury directs the migration,    proliferation, and differentiation of solid organ stem cells:    evidence from the effect of hypoxia-ischemia in the CNS on clonal    “reporter” neural stem cells. Exp Neurol 199, 156-178 (2006).-   A29. Parker, M. A. et al. Expression profile of an    operationally-defined neural stem cell clone. Exp Neurol 194,    320-332 (2005).-   A30. Bromberg, J. F. et al. Stat3 as an oncogene. Cell 98, 295-303    (1999).-   A31. Subramanian, A. et al. Gene set enrichment analysis: a    knowledge-based approach for interpreting genome-wide expression    profiles. Proc Natl Acad Sci USA 102, 15545-15550 (2005).-   A32. Engebraaten, 0., Bjerkvig, R., Pedersen, P. H. & Laerum, 0. D.    Effects of EGF, bFGF, NGF and PDGF(bb) on cell proliferative,    migratory and invasive capacities of human brain-tumour biopsies in    vitro. Int J Cancer 53, 209-214 (1993).-   A33. Jackson, E. L. et al. PDGFR alpha-positive B cells are neural    stem cells in the adult SVZ that form glioma-like growths in    response to increased PDGF signaling. Neuron 51, 187-199 (2006).-   A34. Rothhammer, T., Bataille, F., Spruss, T., Eissner, G. &    Bosserhoff, A. K. Functional implication of BMP4 expression on    angiogenesis in malignant melanoma. Oncogene 26, 4158-4170 (2007).-   A35. Rothhammer, T. et al. Bone morphogenic proteins are    overexpressed in malignant melanoma and promote cell invasion and    migration. Cancer Res 65, 448-456 (2005).-   A36. Lee, J. et al. Tumor stem cells derived from glioblastomas    cultured in bFGF and EGF more closely mirror the phenotype and    genotype of primary tumors than do serum-cultured cell lines. Cancer    Cell 9, 391-403 (2006).-   A37. Pelloski, C. E. et al. YKL-40 expression is associated with    poorer response to radiation and shorter overall survival in    glioblastoma. Clin Cancer Res 11, 3326-3334 (2005).-   A38. Tarin, D., Thompson, E. W. & Newgreen, D. F. The fallacy of    epithelial mesenchymal transition in neoplasia. Cancer Res 65,    5996-6000; discussion 6000-5991 (2005).-   A39. Margolin, A. A. et al. Reverse engineering cellular networks.    Nat Protoc 1, 662-671 (2006).-   A40. Frank, S. R., Schroeder, M., Fernandez, P., Taubert, S. &    Amati, B. Binding of c-Myc to chromatin mediates mitogen-induced    acetylation of histone H4 and gene activation. Genes Dev 15,    2069-2082 (2001).-   A41. Simmons, M. L. et al. Analysis of complex relationships between    age, p53, epidermal growth factor receptor, and survival in    glioblastoma patients. Cancer Res 61, 1122-1128 (2001).-   A42. Zeeberg, B. R. et al. GoMiner: a resource for biological    interpretation of genomic and proteomic data. Genome Biol 4, R28    (2003).

Example 8 A Transcriptional Module Initiates and Maintains MesenchymalTransformation in Brain Tumors

Using a combination of cellular-network reverse-engineering algorithmsand experimental validation assays, a transcriptional module, includingsix transcription factors (TFs) that synergistically regulates themesenchymal signature of malignant glioma was identified. This is apoorly understood molecular phenotype, never observed in normal neuraltissue. It represents the hallmark of tumor aggressiveness in high-gradeglioma, and its upstream regulation is so far unknown. Overall, thenewly discovered transcriptional module regulates >74% of the signaturegenes, while two of its TFs (C/EBPβ and Stat3) display features ofinitiators and master regulators of mesenchymal transformation. Ectopicco-expression of C/EBPβ and Stat3 is sufficient to reprogram neural stemcells along the aberrant mesenchymal lineage, while simultaneouslysuppressing differentiation along the default neural lineages (neuronaland glial). Conversely, silencing the two TFs in human glioma cell linesand glioblastoma-derived tumor initiating cells leads to collapse of themesenchymal signature with corresponding loss of tumor aggressiveness invitro and in immunodeficient mice after intracranial injection. In humantumor samples, combined expression of C/EBPβ and Stat3 correlates withmesenchymal differentiation of primary glioma and is a predictor of poorclinical outcome. Taken together, these results reveal that activationof a small regulatory module—inferred from the accurate reconstructionof transcriptional networks—is necessary and sufficient to initiate andmaintain an aberrant phenotypic state in eukaryotic cells.

High-grade gliomas (HGGs) are the most common brain tumors in humans andare essentially incurable (Ohgaki, 2005; herein incorporated byreference in its entirety). Just as the ability to metastasizeidentifies the highest degree of malignancy in epithelial tumors, thedefining hallmarks of aggressiveness of glioblastoma multiforme (GBM)are local invasion and neo-angiogenesis {Demuth, 2004; Kargiotis, 2006;each herein incorporated by reference in its entirety}. Drivers of thesephenotypic traits include intrinsic autocrine signals produced by braintumor cells to invade the adjacent normal brain and stimulate formationof new blood vessels {Hoelzinger, 2007; herein incorporated by referencein its entirety}. It has been suggested that GBM re-engagespre-established ontogenetic motility and invasion signals that normallyoperate in neural stem cells (NSCs) and immature progenitors {Visted,2003; herein incorporated by reference in its entirety}. A recentlyestablished notion postulates that neoplastic transformation in thecentral nervous system (CNS) converts neural stem cells into cell typesmanifesting a mesenchymal phenotype, a state associated withuncontrolled ability to invade and stimulate angiogenesis {Phillips,2006; Tso, 2006; each herein incorporated by reference in its entirety}.Differentiation along the mesenchymal lineage, however, is virtuallyundetectable in normal neural tissue during development. Specifically,gene expression studies have established that over-expression of a“mesenchymal” gene expression signature (MGES) and loss of a proneuralsignature (PNGES), co-segregate with the poorest prognosis group ofglioma patients {Phillips, 2006; herein incorporated by reference in itsentirety}. The MGES↑PNGES↓ phenotype can thus be referred to as themesenchymal phenotype of high-grade glioma. Without being bound bytheory, drift toward the mesenchymal lineage may be exclusively anaberrant event that occurs during brain tumor progression. Without beingbound by theory, glioma cells may recapitulate the rare mesenchymalplasticity of NSCs {Phillips, 2006; Takashima, 2007; Tso, 2006; Wurmser,2004; each herein incorporated by reference in its entirety}. Themolecular events that trigger activation of the MGES and suppression ofthe PNGES signatures, imparting a highly aggressive phenotype to gliomacells, remain unknown.

To discover transcription factors (TFs) causally linked tooverexpression of MGES genes, the conventional paradigm of geneexpression profile-based cancer research was inverted. Rather thanasking which genes comprise the MGES, a genome-wide, glioma-specific mapof transcriptional interactions was inferred and then interrogated toidentify TFs controlling MGES induction in vivo. Efforts to identify TFsassociated with specific cancer signatures from regulatory networks haveyet to produce experimentally validated discoveries, likely becausethese networks are still poorly mapped, especially within specificmammalian cellular contexts {Rhodes, 2005; herein incorporated byreference in its entirety}. However, extension of reverse engineeringapproaches to the genome-wide inference of regulatory networks inmammalian cells have recently shown some promise {Basso, 2005, Margolin,2006; each herein incorporated by reference in its entirety}. Thesemethods have been further refined to identify causal, rather thanassociative interactions {Margolin, 2006; herein incorporated byreference in its entirety}, and have been successfully applied to theidentification of dysregulated genes within developmental andtumor-related pathways {Zhao, 2009, Lim, 2009, Mani, 2007, Palomero,2006, Taylor, 2008; each herein incorporated by reference in itsentirety}. It was reasoned that the context-specific regulatory networksinferred by these algorithms may provide sufficient accuracy to allowestimating (a) the activity of TFs from that of their transcriptionaltargets or regulons and (b) TFs that are Master Regulators (MRs) ofspecific eukaryotic signatures {Hanauer, 2007; Lander, 2004; each hereinincorporated by reference in its entirety} from the overlap betweentheir regulons and the signatures. Thus, by studying the overlap betweenthe MGES of malignant glioma and the computationally-inferred regulon ofeach TF, the aim was to unravel the complement of primary TFs activatedand suppressed in that phenotype and, more specifically, thoseassociated with its induction in human brain tumors.

TFs causally linked with MGES activation were first identified using thepublished dataset {Phillips, 2006; herein incorporated by reference inits entirety}. Next, it was discovered that the same TFs are associatedwith induction of a poor prognosis signature in the distinct GBM sampleset from the Atlas-TCGA consortium {Network, 2008; herein incorporatedby reference in its entirety}. Comprehensive computational andexperimental assays converged on two of these TFs (C/EBP and Stat3) assynergistic initiators and essential MRs of the MGES of human glioma.Indeed, ectopic co-expression of the two factors in NSCs was sufficientto initiate expression of the mesenchymal set of genes, suppressproneural genes, promote mesenchymal transformation and triggerinvasion. Conversely, silencing of these TFs consistently depletedGBM-derived brain tumor initiating cells (GBM-BTICs) and glioma celllines of mesenchymal attributes and greatly impaired their ability toinitiate brain tumor formation after intracranial transplantation in themouse brain. Most notably, independent immunohistochemistry experimentsin 62 human glioma specimens showed that concurrent expression of C/EBPβand Stat3 is significantly associated to the expression of mesenchymalproteins and is an accurate predictor of the poorest outcome of gliomapatients.

Computational Identification of the Transcriptional Regulation ModuleDriving the Mesenchymal Signature of High-Grade Glioma.

To identify the causal events that activate the MGES in HGGs, whethercopy number variation alone may account for the aberrant expression ofall or some of its genes was first asked. Integrated analysis of 76 HGGsfor gene expression profiling and array comparative genomichybridization (aCGH) failed to show any correlation between meanexpression value and DNA copy number of MGES genes in tumors from any ofthe molecular subgroups (proneural, mesenchymal, and proliferative, seeMethods and FIG. 23). Thus, it was sought to identify candidate MR-TFs,which may functionally activate the MGES in HGGs, using an unbiasedcomputational approach.

The ARACNe reverse-engineering algorithm {Basso, 2005; hereinincorporated by reference in its entirety} was used to assemble agenome-wide repertoire of HGGs-specific transcriptional interactions(the HGG-interactome), from 176 gene expression profiles of grade III(anaplastic astrocytoma) and grade IV (GBM) samples {Freije, 2004;Nigro, 2005; Phillips, 2006; each herein incorporated by reference inits entirety}. These specimens had been previously classified into threemolecular signature groups—proneural, proliferative, andmesenchymal—based on the coordinated expression of specific gene sets byunsupervised cluster analysis {Phillips, 2006; herein incorporated byreference in its entirety} (see Table 3A-C). ARACNe is an informationtheoretic approach for the inference of TF-target interactions fromlarge sets of microarray expression profiles. It previously identifiedtargets of MYC and NOTCH 1 in B and T cells respectively, which wereexperimentally validated {Basso, 2005; Palomero, 2006; each hereinincorporated by reference in its entirety}. It was later refined toinfer directed (i.e. causal) interactions by considering only thoseinvolving at least one GO-annotated TF {Ashburner, 2000; hereinincorporated by reference in its entirety} (see Methods) and by assumingthat direct information transfer between mRNA species is mostly mediatedby transcriptional interactions {Margolin, 2006; herein incorporated byreference in its entirety}. Thus, all interactions in theHGG-interactome, except those between two TFs (<10% of the total), aredirected and thus explicitly model causality. These included 117,789transcriptional interactions, 1,563 of which were between TFs and 102 ofthe 149 MGES genes {Phillips, 2006; herein incorporated by reference inits entirety} represented across all the gene expression profile data.

Next, a Master Regulator Analysis (MRA) algorithm was applied to theHGG-interactome (see Methods). The algorithm used the statisticalsignificance of the overlap between each TF regulon (the ARACNe-inferredtargets of the TF) and the MGES genes (MGES-enrichment) to infer the TFsthat are more likely to regulate signature activity. Enrichment p-valueswere measured by Fisher Exact Test (FET). From a list of 928 TFs (Table4), the MRA inferred 53 MGES-specific TFs, at a False Discovery Rate(FDR) <5% (Table 5A). These were ranked based on the total number ofMGES targets they regulated. The top six TFs (Stat3, C/EBPβ/δ, bHLH-B2,Runx1, FosL2, and ZNF238) collectively controlled >74% of the MGESgenes, suggesting that a signature core may be controlled by arelatively small number of TFs (FIG. 1). Consistent with theirpreviously reported activity {Aoki, 1998; Fuks, 2001; each hereinincorporated by reference in its entirety}, Spearman correlationanalysis revealed that five of these TFs are likely activators (Stat3,C/EBPβ/δ, bHLH-B2, Runx1, and FosL2) and one is likely a repressor(ZNF238). Overlap between the regulons of the six TFs was highlysignificant (Table 6), suggesting coordinated and potentiallysynergistic regulation of the MGES. Both C/EBPβ and C/EBPδ were amongthe most MGES-enriched TFs. These are known to form stoichiometric homoand heterodimers with identical DNA binding specificity and redundanttranscriptional activity {Ramji, 2002; herein incorporated by referencein its entirety}. One can thus use the term C/EBP to indicate the TFcomplex and the union of their targets as the corresponding regulon.

Similar MRA analysis of the Proneural (PNGES) and Proliferative (PROGES)signatures of HGGs was conducted (Table 7). Virtually no overlap amongcandidate MRs of the three signatures was detected, with the notableexception of a handful of TFs inversely associated with MGES and PNGESactivation (OLIG2, for instance, activates 46 proneural and represses 12mesenchymal genes, respectively). These results are consistent with thenotion that proneural and mesenchymal genes in HGGs are mutuallyexclusive {Phillips, 2006; herein incorporated by reference in itsentirety}. It also indicates that the reconstruction of the networktopology and the application of the MRA algorithm to HGG samples are notbiased towards the identification of specific TFs. Note that the impactof potential false negatives from ARACNe is considerably reduced sinceMRA analysis is based on enrichment criteria rather than on theidentification of specific targets.

Inference of Regulatory Programs Controlling Individual MGES Genes.

Stepwise linear regression (SLR) was then used to infer simple,quantitative regulation models for each MGES gene (i.e. a regulatoryprogram). In these models, the log-expression of each MGES gene isapproximated by a linear combination of the log-expression of 53ARACNe-inferred and 52 additional TFs, whose DNA-binding signature wasenriched in MGES gene promoters (see Methods). Six TFs were in bothlists, for a total of 99 TFs (Table 5B). The log-transformation allowsconvenient linear representation of multiplicative interactions betweenTF activities {Bussemaker, 2001; Tegner, 2003; each herein incorporatedby reference in its entirety}. TFs were individually added to the model,each time selecting the one contributing the most significant reductionin relative expression error (predicted vs. observed), untilerror-reduction was no longer significant. Thus, expression of each MGESgene was defined as a function of a small number of TFs (1 to 5).Finally, TFs were ranked based on the fraction of MGES genes theyregulated. Surprisingly, the top six MRA-inferred TFs were also amongthe eight controlling the largest number of MGES targets, based on SLRanalysis (Table 8). This finding provides further support for aregulatory role of these TFs in the control of the MGES. Among them, thethree TFs with the highest linear-regression coefficient values wereC/EBP (α=0.40), bHLH-B2 (α=0.41), and Stat3 (α=0.40), thus establishingthem as likely MGES-MR candidates. The next strongest TF, ZNF238, had anegative coefficient (α=−0.34) confirming its role as a strong MGESrepressor.

Biochemical and Functional Validation of the ARACNe/MRA RegulatoryModule.

It was sought to experimentally validate the TFs inferred as positiveregulators of the MGES in HGGs. The first consideration was whetherthese TFs could bind the promoter region (proximal regulatory DNA) oftheir predicted MGES targets. Target promoters were first analyzed insilico to identify putative binding sites (see Methods). ChromatinImmunoprecipitation (ChIP) assays were then performed near predictedsites in a human glioma cell line to validate the ARACNe-inferredtargets of four of the five TFs (C/EBPβ, Stat3, bHLH-B2, and FosL2), forwhich appropriate reagents were available. On average, TF-specificantibodies (but not control antibodies) immunoprecipitated with 80% ofthe tested genomic regions (FIG. 3). Given that binding may occur viaco-factors, via non-canonical binding sites, or outside the selectedregion, this provides a conservative lower-bound on the number of theirbound MGES targets.

Next, lentivirus-mediated shRNA silencing of the five TFs (C/EBPβ,Stat3, bHLH-B2, FosL2, and Runx1) was performed in the SNB19 humanglioma cell line, followed by gene expression profiling using HT-12v3Illumina BeadArrays in triplicate. GSEA analysis revealed: (a) thatgenes differentially expressed following shRNA-mediated silencing ofeach TF were enriched in its ARACNe-inferred regulon genes (but not inthose of equivalent control TFs) (Table 9A); (b) that, consistent withpredicted TF-regulon overlap, cross-enrichment among the TFs was alsosignificant (Table 9A), suggesting that these TFs may work as aregulatory module; and (c) that genes differentially expressed followingsilencing of each TF were also enriched in MGES genes (Table 9B). Takentogether, these results suggest that ARACNe and MRA accurately predictedthe modular regulation of the MGES by these five TFs in malignantglioma.

TFs Controlling MGES in Malignant Glioma Form a Highly Connected andHierarchically Organized Module.

It was considered whether the inferred TFs could be organized into aregulatory module. ChIP assays revealed that C/EBPβ and Stat3 occupytheir own promoter and are thus likely involved in autoregulatory (AR)loops (FIG. 4A-B). Additionally, Stat3 occupies the FosL2 and Runx1promoters (FIG. 4A); C/EBPβ occupies those of Stat3, FosL2, bHLH-B2,C/EBPβ, and C/EBPβ, thus confirming the redundant autoregulatoryactivity of the two C/EBP subunits (FIG. 4B) {Niehof, 2001; Ramji, 2002;each herein incorporated by reference in its entirety}; FosL2 occupiesthose of Runx1 and bHLH-B2 (FIG. 4C) and bHLH-B2 occupies only thepromoter of Runx1 (FIG. 4D). The MGES regulatory-control topology thatemerges from promoter occupancy analysis is highly modular, with 8 of 10possible intra-module interactions implemented (p=1.0×10⁻⁸ by FET, basedon the ratio of intra- vs. inter-module interactions for equallyconnected TFs) and displays a clearly hierarchical structure (FIG. 4E).At the very top of this hierarchical control structure, we find C/EBPand Stat3, which are also involved in AR loops and form feed-forward(FF) loops with the largest fraction of MGES genes (43%) than any of theother TF-pairs. Accordingly, shRNA-mediated co-silencing of C/EBPβ andStat3 in glioma cells produced >2-fold reduction of the levels of themRNAs coding for the second layer TFs in the FF loops (bHLH-B2, FosL2,and Runx1), thus further supporting a hierarchical modular structure(FIG. 16A). Whether C/EBPβ and Stat3 bound the promoters of their MGEStargets also in primary tumors was tested. Experimental conditions weredeveloped to perform C/EBPβ and Stat3 ChIP assays in two human GBMsamples belonging to the mesenchymal signature group. These assaysconfirmed that C/EBPβ and Stat3 bind to their inferred MGES targets alsoin this in vivo context (FIG. 28).

Cross-Species Integrative Analysis of Mouse and Human Cells CarryingPerturbations of C/EBPβ and Stat3.

The above results suggest that C/EBPβ and Stat3 may operate ascooperative and possibly synergistic MRs of MGES activation in malignantglioma. To functionally validate this hypothesis, gain andloss-of-function experiments were conducted for the two TFs in NSCs andhuman glioma cells, respectively. NSCs have been proposed as the cell oforigin for malignant glioma in the mesenchymal subgroup {Phillips, 2006;herein incorporated by reference in its entirety}. Two populations ofmurine NSCs were infected with retroviruses expressing C/EBPβ and aconstitutively active form of Stat3 (Stat3C) {Bromberg, 1999; hereinincorporated by reference in its entirety}. These included an earlypassage of the stable, clonal population of v-myc immortalized mouseNSCs known as C17.2 {Lee, 2007; Park, 2006; Parker, 2005; each hereinincorporated by reference in its entirety} as well as primary murineNSCs derived from the mouse telencephalon at embryonic day 13.5.

For loss-of-function experiments, lentivirus-mediated shRNA silencing ofC/EBPβ and Stat3 in the human glioma cell line SNB19 and inearly-passage cultures of tumor cells derived from primary GBM wasperformed. The latter were grown in serum-free conditions, in thepresence of the growth factors bFGF and EGF. These culture conditionspreserve the tumor stem cell-like features of GBM-derived cells andpropel the formation of GBM-like tumors after intracranialtransplantation in immunodeficient mice {Lee, 2006; herein incorporatedby reference in its entirety} (GBM-derived brain tumor initiating cells,GBM-BTICs, see FIG. 22 for the analysis of their tumor-initiatingcapacity). At least three replicates for each condition were producedand a global dataset of 89 individual samples was generated, including55 knockdown experiments in human glioma cells and 34 ectopic expressionexperiments in mouse NSCs. Gene expression profiles of human sampleswere produced with the HT-12v3 Illumina BeadArrays (including 24,385human genes), while murine samples were profiled on mouse-6V2 IlluminaBeadArrays (including 20,311 mouse genes). 14,857 murine genes weremapped to human orthologs, using the homologene database(http://www.ncbi.nlm.nih.gov/homologene; herein incorporated byreference in its entirety). Of the 149 genes in the MGES, 118 could bemapped to murine genes represented on the mouse-6V2 array.

Quantitative RT-PCR (qRT-PCR) analysis performed on each sample showedthat C/EBPβ and Stat3 were effectively silenced and overexpressed (Table10). Following C/EBPβ shRNA silencing in GBM-BTICs and SNB19, C/EBPβmRNA levels measured by qRT-PCR were significantly reduced compared tonon-target control transduced cells (fold ratio=0.26, p≦0.00108, byU-test). Slightly stronger reduction was observed for Stat3 mRNA inStat3-shRNA silenced cells (fold ratio=0.205, p≦0.00109, U-test).Reciprocal changes followed ectopic expression of the two TFs in C17.2and NSC cells (Table 10) qRT-PCR values and microarray-basedmeasurements were highly correlated for Stat3 but not for C/EBPβ mRNA(FIG. 24). Moreover, the Stat3C and C/EBPβ constructs used in theectopic expression experiments in mouse NSCs lack the 3′ UTR sequencetargeted by the Illumina probes. Thus, the qRT-PCR values for C/EBPβ andStat3 were used, rather than the microarray measurements, as moreaccurate read-outs for their mRNA expression across the 89 samples.

First, it was considered whether this large set of experimentsdemonstrated specific regulation of C/EBPβ and Stat3 ARACNe-inferredtargets. GSEA analysis confirmed that genes co-expressed with the twoTFs across the 89 samples were significantly enriched in theirrespective ARACNe-inferred regulon genes but not in those of control TFs(Table 11). More importantly, the GSEA analysis showed that perturbationof either C/EBPβ (FIG. 17A, FIG. 17D) or Stat3 (FIG. 17B, FIG. 17E)affected the MGES signature specifically (p=2.67×10⁻² and p=2.0×10⁻⁴,respectively by GSEA). Interestingly, common targets of both C/EBP andStat3 were 8-fold more enriched in MGES genes than targets controlledindividually by each TF (FIG. 17G) (p=2.25×10⁻⁵), suggesting synergisticregulation. To test whether the two TFs may be involved in synergisticMGES control, a metagene (C/EBPβ×Stat3) was created whose expression wasproportional to the product of their mRNAs. The expression profile ofany target regulated synergistically by the two TFs (i.e., bymultiplicative rather than additive logic) should be highly correlatedwith such a metagene (FIG. 17C). GSEA analysis confirmed that genesranked by Spearman correlation to the C/EBPPxStat3 metagene weresignificantly enriched in MGES genes (FIG. 17F). This suggests that atleast a subset of the MGES follows a multiplicative (synergistic) modelof regulation, while another subset may be individually regulated byC/EBPβ or Stat3 (complementarity). Taken together, these experimentssupport a cooperative and synergistic control of the MGES by C/EBPβ andStat3 across a large subset of murine NSC and human glioma contexts,with MGES genes responding to both silencing and overexpression of thetwo TFs.

Signature and Dataset-Independent Validation of the Identification ofMRs in HGG.

The MGES was originally identified as common biological attribute of afraction of the samples associated with the poorest prognosis group ofHGGs. It was sought to establish whether i) MRs inferred by theprocedure would also be inferred when using an entirely independentglioma sample datasets and it) MRs identified purely on the basis ofclinical outcome would overlap significantly with those inferred fromanalysis of the MGES signature. The MRA and SLR approaches were thusapplied to the independent glioma dataset provided by the Atlas-TCGAconsortium {Network, 2008; herein incorporated by reference in itsentirety}. This dataset includes 77 and 21 samples associated withworst- and best-prognosis, respectively (92 samples with intermediateprognosis were not considered). Differential expression analysisidentified a TCGA Worst-Prognosis Signature (TWPS), comprising 884 genesdifferentially expressed in the worst-prognosis samples compared to thebest-prognosis ones (p≦0.05 by Student's t-test, Table 12).

GSEA analysis confirmed that MGES genes identified in Phillips, 2006;herein incorporated by reference in its entirety were markedly enrichedin the TWPS signature (p≦1.0×10⁻⁴, FIG. 25), suggesting that thepoor-prognosis group in the Atlas-TCGA dataset also displays a markedlymesenchymal phenotype. However, overlap between MGES and TWPS genes waspartial (22.8%), indicating that other previously unrecognized“mesenchymal” genes should be added to the MGES and/or that otherbiologically relevant functions may cooperate with mesenchymaltransformation to produce the poor-prognosis cluster of HGGs.Nonetheless, five of the 10 most significant MRs identified by MRAanalysis from the original dataset, including 4 out of 5 of our positiveMGES modulators (C/EBPβ, C/EBPδ, Stat3, bHLH-B2, and FosL2), were alsofound among the 10 most significant TFs identified by TWPS-basedanalysis of the Atlas-TCGA dataset. Specifically, C/EBP was inferred asthe most significant TF (C/EBPδ and C/EBPβ were 3^(rd) and 10^(th),respectively), while Stat3 was in 7^(th) position. Additionally, amongthe top 10 TFs, C/EBPβ and C/EBPδ had respectively the first and secondbest linear-regression coefficient by SLR analysis (Table 13). Theseresults suggest significant robustness of the approach both to datasetand signature selection. Furthermore, these findings suggest that theMGES and a more comprehensive signature broadly associated with thepoorest-prognosis are regulated by the same TFs, including C/EBP andStat3 among the top-ranking ones. Recently, there have been severalunsuccessful attempts to identify common expression signatures fromdifferent sample sets representative of the same phenotype {Ein-Dor,2005; herein incorporated by reference in its entirety}. These findingsindicate that MRs of mammalian phenotype signatures may be significantlymore conserved than their specific genes.

Concurrent Expression of Active C/EBPβ and Stat3 Reprograms NSCs Towardthe Mesenchymal Lineage.

Having shown that manipulation of C/EBPβ and Stat3 results incorresponding changes in the MGES, the next question was whether theseeffects are associated with phenotypic changes. First, it was consideredwhether combined and/or individual expression of Stat3C and C/EBPβ inNSCs is sufficient to trigger the mesenchymal phenotypic properties thatcharacterize high-grade gliomas. Ectopic expression of C/EBPβ and Stat3Cin C17.2 NSCs induced dramatic morphologic changes, consistent with lossof ability to differentiate along the default neuronal lineage (FIG. 5A,FIG. 26A). Parental and vector-transfected NSCs have the classicalspindle-shaped morphology that is associated with the neuralstem/progenitor cell phenotype. When grown in the absence of mitogens,these cells display efficient neuronal differentiation characterized byextensive formation of a neuritic network. Conversely, expression ofStat3C and C/EBPβ led to cellular flattening and manifestation of afibroblast-like morphology (FIG. 26A).

Ectopic expression of C/EBPβ and Stat3C cooperatively induced theexpression of mesenchymal markers in NSCs. This was shown withimmunofluorescence staining for SMA and fibronectin in C17.2 expressingthe indicated TFs. SMA positive cells were quantified. For fibronectinimmunostaining, the intensity of fluorescence was quantified. QRT-PCRanalysis of mesenchymal targets in C17.2 expressing the indicated TFs ortransduced with the empty vector was also carried out. Gene expressionwas normalized to the expression of 18S ribosomal RNA.

The morphological changes were associated with gain of the expression ofthe mesenchymal marker proteins SMA and fibronectin and induced mRNAexpression of the mesenchymal genes Chi311/YKL40, Acta2/SMA, CTGF andOSMR. However, the individual expression of Stat3C or C/EBPβ wasgenerally insufficient to induce either mesenchymal marker proteins orexpression of mesenchymal genes. Rather than triggering differentiationalong the neuronal lineage, removal of mitogens toStat3C/C/EBPβ-expressing C17.2 cells resulted in further increase of theexpression of mesenchymal genes and complete acquisition of mesenchymalfeatures such as positive alcian blue staining, a specific assay forchondrocyte differentiation (FIG. 18A-B, FIG. 26A-B). Consistent withthe cellular properties conferred by mesenchymal transformation tomultiple cell types, we found that the expression of Stat3C and C/EBPβrobustly promoted migration in a wound assay and triggered invasionthrough the extracellular matrix in a Matrigel invasion assay (FIG.5C-D). Invasion through Matrigel by C17.2 was stimulated by Stat3C andC/EBPβ in the absence of mitogens or in the presence of PDGF, a knowninducer of cell migration, therefore indicating that theStat3C/C/EBPβ-induced migration and invasion are likely cell intrinsiceffects (FIG. 5D). Next, it was sought to establish the effects ofC/EBPβ and Stat3 in primary NSCs. NSCs isolated from the mouse cortex atembryonic day 13 were cultured and infected with retroviruses expressingStat3C together with a puromycin-resistance gene and/or C/EBPβ togetherwith a green fluorescence protein (GFP). Also in this primary system thecombined but not the individual expression of Stat3C and C/EBPβefficiently induced mesenchymal marker proteins and mesenchymal geneexpression (FIG. 19A-C). Conversely, Stat3C and C/EBPβ abolisheddifferentiation along the neuronal and glial lineages that is normallytriggered in NSCs by removal of mitogens (EGF and bFGF) from the medium(FIG. 19D-F). The C/EBPβ/Stat3C-induced mesenchymal transformation ofprimary NSCs was associated with withdrawal from cell cycle. Thus, thecombined introduction of active C/EBPβ and Stat3 in NSCs preventsdifferentiation along the normal neural lineages and triggersreprogramming toward an aberrant mesenchymal lineage.

C/EBPβ and Stat3 are Essential for Mesenchymal Transformation andAggressiveness of Human Glioma Cells In Vitro, in the Mouse Brain and inPrimary Human Tumors.

To assess the significance of constitutive C/EBPβ and Stat3 in the cellsresponsible for brain tumor growth in humans, it was sought to abolishthe expression of C/EBPβ and Stat3 in cells freshly derived from primaryhuman GBM and grown in serum-free medium, a condition optimal forretention of stem-like properties and tumor initiating ability(GBM-BTICs, see FIG. 7E AND FIG. 21) {Lee, 2006; herein incorporated byreference in its entirety}. Transduction of GBM-BTICs cultures derivedfrom two GBM patients (BTSC-20 and BTSC-3408) with specificshRNA-carrying lentiviruses silenced endogenous C/EBPβ and Stat3 andefficiently eliminated expression of mesenchymal genes and depleted thetumor cells of the mesenchymal marker proteins fibronectin, collagen-5A1and YKL40 (FIG. 20A-D, FIG. 20H, and FIG. 20I). Individual silencing ofC/EBPβ or Stat3 produced variable inhibitory effects with the silencingof C/EBPβ typically carrying the most severe consequences (see forexample the quantitative analysis of YKL40 staining in FIG. 20D).Combined or individual silencing of C/EBPβ and Stat3 in the human gliomacell line SNB19 produced effects similar to those observed in GBM-BTICs(FIG. 20E-G, FIG. 20J).

Next, it was considred whether loss of C/EBPβ and Stat3 in glioma cellsreduced tumor aggressiveness in vitro and in vivo. First, it was foundthat silencing of the two TFs in SNB19 and GBM-BTICs eliminated >70% oftheir ability to invade through Matrigel (FIG. 22A, FIG. 7E). Then, theimpact of C/EBPβ and Stat3 knockdown for brain tumorigenesis in vivo wasdetermined. SNB19 cells transduced with non-targeting control shRNAlentivirus or shRNA targeting C/EBPβ and/or Stat3 were xenografted intothe striatum of immunocompromised mice. Efficient tumor formation wasobserved in all mice injected with shRNA control and shStat3 cells.However, only one of four mice from the shC/EBPβ and one of five micefrom the shC/EBPβ+shStat3 groups developed tumors after 120 days fromthe injection (FIG. 22B). The histologic analysis demonstratedhigh-grade tumors, which displayed peripheral invasion of thesurrounding brain as single cells and cell clusters in the shRNA controlgroup as shown by the staining pattern produced by a human specificvimentin antibody (FIG. 22C). Staining for the endothelial marker CD31revealed marked vascularization in the shRNA control group of tumors.Conversely, the single tumor in the shC/EBPβ+shStat3 group grew wellcircumscribed and was less angiogenic. Tumors in the shStat3 group andthe single tumor in the shC/EBPβ group had an intermediate growthpattern and limited angiogenesis (FIG. 22C-D). Consistent with thenotion that the expression of mesenchymal markers correlates with braintumor aggressiveness, it was found that staining for fibronectin,collagen-5A1 and YKL40 was readily detected in the tumors from thecontrol group but absent or barely detectable in the single tumors fromthe shC/EBPβ and shC/EBPβ+shStat3 groups. Tumors derived from shStat3cells displayed an intermediate phenotype with reduced expression ofmesenchymal markers compared with tumors in the shcontrol group buthigher than that observed in the tumors in the shC/EBPβ andshC/EBPβ+shStat3 groups (shcontrol>shStat3>shC/EBPβ>shC/EBPβ+shStat3).

Intracranial transplantation of GBM-BTICs transduced with shRNA controllentivirus produced extremely invasive tumor cell masses extendingthrough the corpus callosum to the controlateral brain. Combinedknockdown of C/EBPβ and Stat3 led to a significant decrease of the tumorarea and tumor cell density as evaluated by human vimentin staining(FIG. 21B), markedly reduced the proliferation index (FIG. 21A) andabolished the expression of mesenchymal markers fibronectin andcollagen-5A1 (FIG. 21D-E).

As final test for the significance of the expression of C/EBPβ and Stat3for the mesenchymal phenotype and aggressiveness of human glioma, animmunohistochemical analysis was conducted for C/EBPβ and active,phospho-Stat3 in human tumor specimens, and the expression of these TFswas compared with YKL-40 (a well-established mesenchymal proteinexpressed in primary human GBM) {Nigro, 2005; Pelloski, 2005; eachherein incorporated by reference in its entirety} and patient outcome ina collection of 62 newly diagnosed GBMs (FIG. 29A-B). FET analysisshowed that expression of either C/EBPβ or Stat3 were significantlyassociated with YKL-40 expression (C/EBPβ, p=4.9×10⁻⁵; Stat3,p=2.2×10⁻⁴). However, the association was higher when double positivetumors (C/EBPβ+/Stat3+) were compared to double negatives(C/EBPβ−/Stat3−, p=2.7×10⁻⁶). Furthermore, double positive tumors wereassociated with markedly worse clinical outcome than tumors that wereeither single or double negatives (log-rank test, p=0.0002, FIG. 21E).Positivity for either of the two TFs remained predictive of negativeoutcome but with lower statistical strength than double positivity(C/EBPβ, p=0.0022; Stat3, p=0.0017). Together, the above results providecompelling indication that the activities of C/EBPβ and Stat3 areessential to maintain mesenchymal properties and aggressiveness of humanglioma, and mark the worst survival group of GBM patients.

Discussion

Recent progress in systems biology has allowed the reconstruction ofcellular networks proposed to play important functions in variousphenotypic states, including cancer {Ergun, 2007; Rhodes, 2005; eachherein incorporated by reference in its entirety}. However,network-based methods have yet to identify MRs of predefined tumorphenotypes that could withstand rigorous experimental validation.Similarly, synergistic/cooperative regulations of human phenotypes arevirtually unexplored using network-based approaches. Here, it is shownthat context-specific inference of a regulatory network in HGGs can beused to identify a transcriptional regulatory module that controls theexpression of genes associated with the mesenchymal signature andpoorest-prognosis of HGGs. Two of the module TFs, C/EBPβ and Stat3, werefurther characterized as first level controllers of module activity, viaa large number of FF loops, and cooperative/synergistic initiators andMRs of the MGES. FF loops contribute to stabilizing positive regulationof the signature and to making its activity relatively insensitive toshort regulatory fluctuations{Kalir, 2005; Milo, 2002, Science; eachherein incorporated by reference in its entirety}.

In the proposed approach presented here, the traditional paradigm ofgene expression profile based cancer research, yielding long lists ofdifferentially expressed genes (i.e., cancer signatures), becomes astarting point for a cellular-network analysis where a causal regulatorymodel identifies the TFs that control the signatures and relatedphenotypes. As shown, the stability of the MRs across distinct datasetssurpasses by far that of the signature genes. Indeed, poor overlap ofcancer signatures and lack of validation across distinct datasets hasbeen a long-standing concern {Ein-Dor, 2005; herein incorporated byreference in its entirety}. Yet the new approach produced virtuallyidentical regulatory MR modules when applied to two completely distinctdatasets and signatures associated with poor-prognosis in HGGs.Conversely, attempts to test several more conventional statisticalassociation methods failed to identify the two MRs. This suggests thatenrichment analysis of ARACNe-inferred TF regulons is specificallyuseful for the identification of MRs of tumor-related phenotypes. Due tothe hyperexponential complexity in the number of parent regulators,other graph-theoretical methods such as Bayesian Networks may be lesssuited to explore regulatory modules where a large number of TFscooperatively and synergistically determine signature regulation. Theresults do not exclude that such approaches may however provide furtherfine-grain regulatory insight once the number of candidate MRs isreduced to a handful by methods such as those proposed here. Yet, once arelatively small number of TFs is identified, direct experimentalvalidation is feasible and will provide more conclusive results, asshown here.

While such an approach is of general applicability, it also presentssome limitations. For instance, the activity of some TFs may bemodulated only post-translationally, thus preventing the identificationof their targets by ARACNe. Furthermore, due to false negatives, theregulons of some TFs may be too small to detect statisticallysignificant enrichment, thus preventing their identification aspotential MRs. The latter is partially mitigated by the fact that TFswith small regulons may be less likely to produce the broad regulatorychanges associated with phenotypic transformations.

The experimental follow-up established that C/EBPβ and Stat3 aresufficient in NSCs and necessary in human glioma cells for mesenchymaltransformation. Interestingly, C/EBPβ and Stat3 are expressed in thedeveloping nervous system {Barnabe-Heider, 2005; Bonni, 1997; Nadeau,2005; Sterneck, 1998; each herein incorporated by reference in itsentirety}. However, while Stat3 induces astrocyte differentiation andinhibits neuronal differentiation of neural stem/progenitor cells,C/EBPβ promotes neurogenesis and opposes gliogenesis {He, 2005; Menard,2002; Nakashima, 1999; Paquin, 2005; each herein incorporated byreference in its entirety}. How can the combined activity of C/EBPβ andStat3 promote differentiation toward an aberrant lineage (mesenchymal)and oppose the genesis of the normal neural lineages (neuronal andglial)? Without being bound by theory, it is proposed that mesenchymaltransformation results from concurrent activation of two conflictingtranscriptional regulators normally operating to funnel opposing signals(neurogenesis vs. gliogenesis). This scenario is intolerable by normalneural stem/progenitor cells whereas it operates to permanently drivethe mesenchymal phenotype in the context of the genetic and epigeneticchanges that accompany high-grade gliomagenesis (EGFR amplification,PTEN loss, Akt activation, etc.) {Phillips, 2006; herein incorporated byreference in its entirety}.

The finding that C/EBPβ/Stat3C-expressing NSCs become unable todifferentiate along the default neuronal lineage and lose expression ofthe normal proneural signature genes reflects the mutually exclusiveexpression of the proneural and mesenchymal signatures observed inprimary GBM {Phillips, 2006; herein incorporated by reference in itsentirety}. Without being bound by theory, it is proposed that theneuroepithelial to mesenchymal reprogramming induced by C/EBPβ and Stat3recapitulates the epithelial to mesenchymal transition frequentlydescribed in epithelial neoplasms undergoing progression toward a moreinvasive and metastatic tumor type {Tarin, 2005; herein incorporated byreference in its entirety}. Thus, an exciting implication of this workis that, by acting upstream of the mesenchymal genes,C/EBP/Stat3-mediated transcription reprograms the cell fate of NSCstoward an aberrant “mesenchymal” lineage. In the context of othergenetic and epigenetic alterations, this transformation triggers themost aggressive properties of malignant brain tumors, namely invasionand neo-angiogenesis. Since the expression of C/EBPβ and Stat3 in humanglioma cells is essential to maintain the tumor initiating capacity andthe ability to invade the normal brain, the two TFs provide importantclues for diagnostic and pharmacological intervention. Consistent withthis notion, the combined expression of C/EBPβ and Stat3 is linked tothe mesenchymal state of primary GBM and provides an excellentprognostic biomarker for tumor aggressiveness.

In conclusion, the first evidence that computational systems biologymethods can be effectively used to infer MRs that choreograph themalignant transformation of a human cell is presented. This is a generalnew paradigm that will be applicable to the dissection of normal andpathologic phenotypic states.

Methods

ARACNe Network Reconstruction.

ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks),an information-theoretic algorithm for inferring transcriptionalinteractions, was used to identify a repertoire of candidatetranscriptional regulators of the MGES genes. Expression profiles usedin the analysis were previously characterized using Affymetrix HU-133Amicroarrays and preprocessed by MAS 5.0 normalization procedure{Phillips, 2006; herein incorporated by reference in its entirety}.First, candidate interactions between a TF (x) and its potential target(y) are identified by computing pairwise mutual information, MI[x; y],using a Gaussian kernel estimator {Margolin, 2006; herein incorporatedby reference in its entirety} and by thresholding the mutual informationbased on the null-hypothesis of statistical independence (p<0.05Bonferroni corrected for the number of tested pairs). Then, indirectinteractions are removed using the data processing inequality, a wellknown property of the mutual information. For each TF-target pair (x, y)a path through any other TF (z) was considered and any interaction suchthat MI[x; y]<min(MI[x; z], MI[y; z]) was removed.

Transcription Factor Classification.

To identify human transcription factors (TFs), the human genes annotatedas “transcription factor activity” in Gene Ontology and the list of TFsfrom TRANSFAC were selected. From this list, general TFs (e.g. stablecomplexes like polymerases or TATA-box-binding proteins) were removed,and some TFs not annotated by GO were added, producing a final list of928 TFs that were represented on the HG-U133A microarray gene set.

Master Regulator Analysis.

The MRA has two steps. First, for each TF its MGES-enrichment iscomputed as the p-value of the overlap between the TF-regulon and theMGES genes, assessed by Fisher Exact Test (FET). Since FET depends onregulon size, it can be used to assess MGES-enriched TFs but not to rankthem. MGES-enriched TFs are thus ranked based on the total number ofMGES genes in their regulon, under the assumption that TFs controlling alarger fraction of MGES genes will be more likely to determine signatureactivity.

Stepwise Linear Regression (SLR) Analysis.

A regulatory program for each MGES gene was computed as follows: thelog₂ expression of the i-th MGES gene was considered as the responsevariable and the log₂-expression of the TFs as the explanatory variablesin the linear model log x_(i)=Σα_(ij) log f_(j)+β_(ij) {Tegner, 2003}.Here, f_(j) represents the expression of the j-th TF in the model andthe (α_(ij), β_(ij)) are linear coupling coefficients computed bystandard regression analysis. TFs are iteratively added to the model, bychoosing each time the one producing the smallest relative errorE=Σ|x_(i)−x_(i0)|/x_(i0) between predicted and observed targetexpression. This is repeated until the decrease in relative error is nolonger statistically significant, based on permutation testing. To avoidexcessive multiple hypothesis testing correction, TFs were chosen onlyamong the following: (a) the 55 inferred by ARACNe at FDR <0.05 and (b)TFs whose DNA binding signature was significantly enriched in theproximal promoter of the MGES genes and that are expressed in thedataset, based on the coefficient of variation (CV≧0.5). TFs were thenranked based on the number of MGES target they regulated, with theaverage Linear-Regression coefficient providing additional insight.

Cell Lines and Cell Culture Conditions.

SNB75, SNB19, 293T and Phoenix cell lines were grown in DMEM plus 10%Fetal Bovine Serum (FBS, Gibco/BRL). GBM-derived BTICs were grown asneurospheres in Neurobasal media (Invitrogen) containing N2 and B27supplements (Invitrogen), and human recombinant FGF-2 and EGF (50 ng/mleach; Peprotech). Murine neural stem cells (mNSCs) (from an earlypassage of clone C17.2) (27-29; each herein incorporated by reference inits entirety) were cultured in DMEM plus 10% heat inactivated FBS,(Gibco/BRL), 5% Horse serum (Gibco/BRL) and 1% L-Glutamine (Gibco/BRL).Neuronal differentiation of mNSCs was induced by growing cells in DMEMsupplemented with 0.5% Horse serum. For chondrocyte differentiation,cells were treated with STEMPRO chondrogenesis differentiation kit(Gibco/BRL) for 20 days.

Primary murine neural stem cells were isolated from E13.5 mousetelencephalon and cultured in the presence of FGF-2 and EGF (20 ng/mleach) as described {Bachoo, 2002; herein incorporated by reference inits entirety} Differentiation of neural stem cells was induced byculturing neurospheres on laminin-coated dishes in NSC medium in theabsence of growth factors. mNSC expressing Stat3C and C/EBPβ, weregenerated by retroviral infections using supernatant from Phoenixecotropic packaging cells transfected with pBabe-Stat3C-FLAG and/orpLZRS-T7-His-C/EBPβ-2-IRES-GFP.

Promoter Analysis and Chromatin Immunoprecipitation (ChIP).

Promoter analysis was performed using the MatInspector software(www.genomatix.de; herein incorporated by reference in its entirety). Asequence of 2 kb upstream and 2 kb downstream from the transcriptionstart site was analyzed for the presence of putative binding sites foreach TFs. Primers used to amplify sequences surroundings the predictedbinding sites were designed using the Primer3 software(http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi; hereinincorporated by reference in its entirety) and are listed in Table 15.

Chromatin immunoprecipitaion was performed as described in Frank, 2001;herein incorporated by reference in its entirety. SNB75 cells lysateswere precleared with Protein A/G beads (Santa Cruz) and incubated at 4°C. overnight with 1 μl of polyclonal antibody specific for C/EBPβ(sc-150, Santa Cruz), Stat3 (sc-482, Santa Cruz), FosL2 (Fra2, sc-604,Santa Cruz), bHLH-B2 (A300-649A, BETHYL Laboratories), or normal rabbitimmunoglobulins (Santa Cruz). DNA was eluted in 200 μl of water and 1 μlwas analyzed by PCR with Platinum Taq (Invitrogen). For primary GBMsamples, 30 mg of frozen tissue was transferred in a tube with 1 ml ofculture medium, fixed with 1% formaldehyde for 15 min and stopped with0.125 M glycine for 5 min. Samples were centrifuged at 4000 rpm for 2min, washed twice and diluted in PBS. Tissues were homogenized using apestle and suspended in 3 ml of ice-cold immunoprecipitation buffer withprotease inhibitors and sonicated. ChIP was then performed as describedabove.

QRT—PCR and Microarray Analysis.

RNA was prepared with RiboPure kit (Ambion), and used for first strandcDNA synthesis using random primers and SuperScriptll ReverseTranscriptase (Invitrogen). QRT-PCR was performed using Power SYBR GreenPCR Master Mix (Applied Biosystems). Primers are listed in Table 16.QRT-PCR results were analyzed by the ΔΔCT method (Livak & Schmittgen,Methods 25:402, 2001; herein incorporated by reference in its entirety)using GAPDH or 18S as housekeeping genes.

RNA amplification for Array analysis was performed with IlluminaTotalPrep RNA Amplification Kit (Ambion). 1.5 μg of amplified RNA washybridized on Illumina HumanHT-12v3 or MouseWG-6 expression BeadChipaccording to the manufacturer's instructions. Hybridization data wasobtained with an iScan BeadArray scanner (Illumina) and pre-processed byvariance stabilization and robust spline normalization implemented inthe lumi package under the R-system (Du, P., Kibbe, W. A. and Lin, S.M., (2008) ‘lumi: a pipeline for processing Illumina microarray’,Bioinformatics 24(13):1547-1548; herein incorporated by reference in itsentirety).

Immunofluorescence and Immunohistochemistry.

Immunofluorescence staining was performed as previously described{Rothschild, 2006; herein incorporated by reference in its entirety}.Primary antibodies and dilutions were: SMA (mouse monoclonal, Sigma,1:200), Fibronectin (mouse monoclonal, BD Biosciences, 1:200), Tau(rabbit polyclonal, Dako, 1:400), βIIITubulin (mouse monoclonal,Promega, 1:1000), CTGF (rabbit polyclonal, Santa Cruz, 1:200), YKL40(rabbit polyclonal, Quidel, 1:200) and Col5A1 (rabbit polyclonal, SantaCruz, 1:200). Confocal images acquired with a Zeiss Axioscop2 FS MOTmicroscope were used to score positive cells. At least 500 cells werescored for each sample. Quantification of the fibronectin intensitystaining in mNSC was performed using NIH Image J software(http://rsb.info.nih.gov/ij/, NIH, USA; herein incorporated by referencein its entirety). The histogram of the intensity of fluorescence of eachpoint of a representative field for each condition was generated. Thefluorescence intensity of three fields from three independentexperiments was scored, standardized to the number of cells in the fieldand divided by the intensity of the vector. For immunostaining ofxenograft tumors, mice were perfused trans-cardially with 4% PFA, brainswere dissected and post-fixed for 48 h in 4% PFA. Immunostaining wasperformed as previously described {Zhao, 2008; herein incorporated byreference in its entirety}. Primary antibodies and dilutions werefibronectin (mouse moclonal, BD Bioscences, 1; 100), Col5A1 (rabbitpolyclonal, Santa Cruz, 1:100), YKL40 (rabbit polyclonal, Quidel, 1;100), human vimentin (mouse monoclonal, Sigma, 1:50), Ki67 (rabbitpolyclonal, Novocastra laboratories, 1:1000). Quantification of thetumor area was obtained by measuring the human vimentin positive area inthe section using the NIH Image J software (http://rsb.info.nih.gov/ij/,NIH, USA; herein incorporated by reference in its entirety). Five tumorsfor each group were analyzed. For quantification of Ki67, the percentageof positive cells was scored in 5 tumors per each group. In histogramvalues represents the mean values; error bars are standard deviations.Statistical significance was determined by t test (with Welch'sCorrection) using GraphPad Prism 4.0 software (GraphPad Inc., San Diego,Calif.). Immunohistochemistry of primary human GBM was performed aspreviously described {Simmons, 2001; herein incorporated by reference inits entirety}. The primary antibodies and dilutions were anti-YKL-40(rabbit polyclonal, Quidel, 1:750), anti C/EBPβ, (rabbit polyclonal,Santa Cruz, 1:250) and anti-p-Stat3 (rabbit monoclonal, Cell Signaling,1; 25), Scoring for YKL-40 was based on a 3-tiered system, where 0 was<5% of tumor cells positive, 1 was 5-30% positivity and 2 was >30% oftumor cells positive. Scores of 1 and 2 were later collapsed into asingle value for display purposes on Kaplan-Meier curves. Associationsbetween C/EBPβ/Stat3 and YKL-40 were assessed using the Fisher exacttest (FET). Associations between C/EBPβ/Stat3 and patients survival wereassessed using the log-rank (Mantel-Cox) test of equality of survivaldistributions.

Migration and Invasion Assays.

For the wound assay testing migration, mNSCs were plated in 60 mm dishesand grown until 95% confluence. A scratch of approximately 1000 μm wasmade with a P1000 pipet tip and images were taken every 24 h with aninverted microscope. For the Matrigel invasion assay, mNSCs and SNB19(1×10⁴) were added to the upper compartment of a 24 well BioCoatMatrigel Invasion Chamber (BD Bioscences) in serum free DMEM. The lowercompartment of the chamber was filled with DMEM containing either 0.5%horse serum or 20 μg/ml PDGF-BB (R&D systems) as chemoattractant. After24 h, invading cells were fixed, stained according to the manufacturer'sinstructions and counted. For GBM-derived BTICs, 5×10⁴ cells were platedon the upper chamber in the absence of growth factors. In the lowercompartment Neurobasal medium containing B27 and N2 supplements plus 20μg/ml PDGF-BB (R&D systems) was used as chemoattractant.

Lentivirus Infection.

Lentiviral expression vectors carrying shRNAs were purchased from Sigma.The sequences are listed in Table 17. To generate lentiviral particles,each shRNA expression plasmid was co-transfected with pCMV-dR8.91 andpCMV-MD2.G vectors into human embryonic kidney 293T cells using Fugene 6(Roche). Lentiviral infections were performed as described {Zhao, 2008;herein incorporated by reference in its entirety}.

Intracranial Injection.

Intracranial injection of SNB19 glioma cell line and GBM-derived BTICswas performed in 6-8 weeks NOD/SCID mice (Charles River laboratories) inaccordance with guidelines of the International Agency for Reserch onCancer's Animal Care and Use Committee. Briefly, 48 h after lentiviralinfection, 2×10⁵ SNB19 or 3×10⁵ BTICs were injected 2 mm lateral and 0.5mm anterior to the bregma, 3 mm below the skull. Mice were monitoreddaily and sacrificed when neurological symptoms appeared. Kaplan-Meiersurvival curve of the mice injected with SNB19 glioma cells wasgenerated using the DNA Statview software package (AbacusConcepts,Berkeley Calif.).

TABLE 3A Table 3A. Genes in the MGES signature. AffyID Gene Symbol GeneID MRA Illumina 200660_at S100A11 6282 * 200808_s_at ZYX 7791 * *200859_x_at FLNA 2316 * * 200879_s_at EPAS1 2034 * * 200974_at ACTA259 * 201058_s_at MYL9 10398 * 201169_s_at BHLHB2 8553 * * 201204_s_atRRBP1 6238 * * 201315_x_at IFITM2 10581 * * 201389_at ITGA5 3678 * *201473_at JUNB 3726 * * 201474_s_at ITGA3 3675 * * 201645_at TNC3371 * * 201666_at TIMP1 7076 * * 201750_s_at ECE1 1889 * * 202180_s_atMVP 9961 * * 202627_s_at SERPINE1 5054 * * 202628_s_at SERPINE1 5054 * *202637_s_at ICAM1 3383 * * 202638_s_at ICAM1 3383 * * 202669_s_at EFNB21948 * * 202765_s_at FBN1 2200 * 202771_at FAM38A 9780 * 202827_s_atMMP14 4323 * * 202833_s_at SERPINA1 5265 * * 202856_s_at SLC16A39123 * * 202888_s_at ANPEP 290 * * 202910_s_at CD97 976 * * 203370_s_atPDLIM7 9260 * 203691_at PI3 5266 * 203729_at EMP3 2014 * * 203828_s_atIL32 9235 * 203835_at LRRC32 2615 * 203887_s_at THBD 7056 * * 203888_atTHBD 7056 * * 204036_at LPAR1 1902 * * 204037_at LPAR1 1902 * *204166_at SBNO2 22904 * * 204293_at SGSH 6448 * * 204306_s_at CD151977 * * 204879_at PDPN 10630 * 204908_s_at BCL3 602 * * 204981_atSLC22A18 5002 * * 205226_at PDGFRL 5157 * * 205266_at LIF 3976 * *205418_at FES 2242 * * 205463_s_at PDGFA 5154 * 205547_s_at TAGLN6876 * * 205572_at ANGPT2 285 * * 205580_s_at HRH1 3269 * * 205729_atOSMR 9180 * * 205936_s_at HK3 3101 * * 206178_at PLA2G5 5322 * *206306_at RYR3 6263 * * 206359_at SOCS3 9021 * * 207714_s_at SERPINH1871 * * 208394_x_at ESM1 11082 * * 208637_x_at ACTN1 87 * * 208789_atPTRF 284119 * * 208790_s_at PTRF 284119 * * 209356_x_at EFEMP2 30008 * *209359_x_at RUNX1 861 * 209360_s_at RUNX1 861 * 209395_at CHI3L11116 * * 209396_s_at CHI3L1 1116 * * 209626_s_at OSBPL3 26031 * *209663_s_at ITGA7 3679 * * 210287_s_at FLT1 2321 * * 210510_s_at NRP18829 * * 210735_s_at CA12 771 * * 210762_s_at DLC1 10395 * * 210772_atFPR2 2358 * * 210845_s_at PLAUR 5329 * * 210992_x_at FCGR2C 9103 *211012_s_at PML 5371 * 211148_s_at ANGPT2 285 * * 211160_x_at ACTN187 * * 211429_s_at SERPINA1 5265 * * 211564_s_at PDLIM4 8572 * *211668_s_at PLAU 5328 * * 211844_s_at NRP2 8828 * * 211924_s_at PLAUR5329 * * 211926_s_at MYH9 4627 * 211964_at COL4A2 1284 * * 211966_atCOL4A2 1284 * * 211980_at COL4A1 1282 * * 211981_at COL4A1 1282 * *212067_s_at C1R 715 * * 212203_x_at IFITM3 10410 * * 212647_at RRAS6237 * * 212951_at GPR116 221395 * * 213746_s_at FLNA 2316 * * 213895_atEMP1 2012 * * 214196_s_at TPP1 1200 * * 214660_at PELO 53918 * *214752_x_at FLNA 2316 * * 214853_s_at SHC1 6464 * * 215498_s_at MAP2K35606 * * 215760_s_at SBNO2 22904 * * 215870_s_at PLA2G5 5322 * *216331_at ITGA7 3679 * * 217867_x_at BACE2 25825 * * 217875_s_at PMEPA156937 * * 218272_at TTC38 55020 * 218424_s_at STEAP3 55240 * * 218880_atFOSL2 2355 * * 218983_at C1RL 51279 * * 219025_at CD248 57124 * *219042_at LZTS1 11178 * * 219566_at PLEKHF1 79156 * * 219869_s_atSLC39A8 64116 * * 220442_at GALNT4 8693 * * 220681_at C22orf26 55267 *220975_s_at C1QTNF1 114897 * * 221293_s_at DEF6 50619 * * 221807_s_atTRABD 80305 * * 221870_at EHD2 30846 * * 221898_at PDPN 10630 *221920_s_at SLC25A37 51312 * * 222206_s_at NCLN 56926 * 222222_s_atHOMER3 9454 * 222528_s_at SLC25A37 51312 * * 222723_at LOC727901 727901222817_at HSD3B7 80270 * 223321_s_at FGFRL1 53834 * 223333_s_at ANGPTL451129 * * 223994_s_at SLC12A9 56996 * 224197_s_at C1QTNF1 114897 * *224710_at RAB34 83871 * 224822_at DLC1 10395 * * 224942_at PAPPA5069 * * 225262_at FOSL2 2355 * * 225548_at SHROOM3 57619 * 225868_atTRIM47 91107 * 225869_s_at UNC93B1 81622 * * 225955_at METRNL 284207 *226328_at KLF16 83855 * 226401_at PARP10 84875 226498_at FLT1 2321 * *226621_at FGG 2266 * * 226658_at PDPN 10630 * 226722_at FAM20C 56975 *227055_at METTL7B 196410 * 227272_at C15orf52 388115 227325_at LOC255783255783 227345_at TNFRSF10D 8793 * 227458_at PDCD1LG1 29126 * 227592_atALDH16A1 126133 * 227697_at SOCS3 9021 * * 228498_at B4GALT1 2683 * *229438_at LOC100132244 100132244 229661_at SALL4 57167 * 230046_atSPRED3 399473 * 230283_at NEURL2 140825 * 230501_at — — 231420_at GGN199720 * 231698_at FLJ36848 647115 231876_at TRIM56 81844 * 232078_atPVRL2 5819 * * 232079_s_at PVRL2 5819 * * 232545_at LRRC29 26231232748_at PAPPA 5069 * * 233695_s_at CECR2 27443 * 235417_at SPOCD190853 235489_at RHOJ 57381 * 238938_at THADA 63892 * * 239507_atLOC151300 151300 241645_at MYO1D 4642 * * 243033_at TWF1 5756 41469_atPI3 5266 *

TABLE 3B Table 3B. Genes in the PNGES signature. Gene AffyID Symbol GeneID 200612_s_at AP2B1 163 200831_s_at SCD 6319 200946_x_at GLUD1 2746200965_s_at ABLIM1 3983 201718_s_at EPB41L2 2037 201830_s_at NET1 10276202022_at ALDOC 230 202178_at PRKCZ 5590 202455_at HDAC5 10014203146_s_at GABBR1 2550 203381_s_at APOE 348 203382_s_at APOE 348203485_at RTN1 6252 203609_s_at ALDH5A1 7915 203619_s_at FAIM2 23017203631_s_at GPRC5B 51704 203853_s_at GAB2 9846 203928_x_at MAPT 4137203929_s_at MAPT 4137 204072_s_at FRY 10129 204100_at THRA 7067204134_at PDE2A 5138 204411_at KIF21B 23046 204513_s_at ELMO1 9844204749_at NAP1L3 4675 204754_at HLF 3131 204762_s_at GNAO1 2775204953_at SNAP91 9892 205050_s_at MAPK8IP2 23542 205110_s_at FGF13 2258205278_at GAD1 2571 205289_at BMP2 650 205290_s_at BMP2 650 205318_atKIF5A 3798 205330_at MN1 4330 205358_at GRIA2 2891 205575_at C1QL1 10882205730_s_at ABLIM3 22885 205751_at SH3GL2 6456 205754_at F2 2147205903_s_at KCNN3 3782 205960_at PDK4 5166 206103_at RAC3 5881 206117_atTPM1 7168 206137_at RIMS2 9699 206196_s_at RUNDC3A 10900 206243_at TIMP47079 206298_at ARHGAP22 58504 206320_s_at SMAD9 4093 206355_at GNAL 2774206356_s_at GNAL 2774 206401_s_at MAPT 4137 206453_s_at NDRG2 57447206518_s_at RGS9 8787 206604_at OVOL1 5017 206785_s_at KLRC1 3821206850_at RASL10A 10633 207091_at P2RX7 5027 207093_s_at OMG 4974207210_at GABRA3 2556 207276_at CDR1 1038 207302_at SGCG 6445207414_s_at PCSK6 5046 207501_s_at FGF12 2257 207723_s_at KLRC3 3823208017_s_at MCF2 4168 208102_s_at PSD 5662 208552_at GRIK4 2900209283_at CRYAB 1410 209293_x_at ID4 3400 209347_s_at MAF 4094209504_s_at PLEKHB1 58473 209558_s_at HIP1R 9026 209610_s_at SLC1A4 6509209611_s_at SLC1A4 6509 209839_at DNM3 26052 209889_at SEC31B 25956209981_at CSDC2 27254 209987_s_at ASCL1 429 209988_s_at ASCL1 429209991_x_at GABBR2 9568 210035_s_at RPL5 6125 210222_s_at RTN1 6252210414_at FLRT1 23769 210432_s_at SCN3A 6328 210657_s_at SEPT4 5414210753_s_at EPHB1 2047 210815_s_at CALCRL 10203 211006_s_at KCNB1 3745211162_x_at SCD 6319 211184_s_at USH1C 10083 211203_s_at CNTN1 1272211484_s_at DSCAM 1826 211520_s_at GRIA1 2890 211663_x_at PTGDS 5730211679_x_at GABBR2 9568 211708_s_at SCD 6319 211748_x_at PTGDS 5730211819_s_at SORBS1 10580 211898_s_at EPHB1 2047 211925_s_at PLCB1 23236212187_x_at PTGDS 5730 212419_at ZCCHC24 219654 212611_at DTX4 23220212812_at SERINC5 256987 212884_x_at APOE 348 212914_at CBX7 23492213091_at CRTC1 23373 213217_at ADCY2 108 213222_at PLCB1 23236213411_at ADAM22 53616 213433_at ARL3 403 213486_at COPG2IT1 53844213549_at SLC18A2 6571 213601_at SLIT1 6585 213664_at SLC1A1 6505213724_s_at PDK2 5164 213744_at ATRNL1 26033 213824_at OLIG2 10215213825_at OLIG2 10215 213841_at — — 213880_at LGR5 8549 213904_at — —213924_at MPPE1 65258 214046_at FUT9 10690 214071_at MPPE1 65258214111_at OPCML 4978 214162_at LOC284244 284244 214251_s_at NUMA1 4926214279_s_at NDRG2 57447 214376_at — — 214434_at HSPA12A 259217214487_s_at RAP2A 5911 214589_at FGF12 2257 214680_at NTRK2 4915214762_at ATP6V1G2 534 214834_at PAR5 8123 214874_at PKP4 8502 214914_atFAM13C1 220965 214930_at SLITRK5 26050 214952_at NCAM1 4684 214954_atSUSD5 26032 215306_at — — 215323_at LUZP2 338645 215444_s_at TRIM3111074 215469_at — — 215522_at SORCS3 22986 215687_x_at PLCB1 23236215767_at ZNF804A 91752 215785_s_at CYFIP2 26999 215789_s_at AJAP1 55966215794_x_at GLUD2 2747 216594_x_at AKR1C1 1645 216925_s_at TAL1 6886217077_s_at GABBR2 9568 217359_s_at NCAM1 4684 217455_s_at SSTR2 6752217681_at WNT7B 7477 217897_at FXYD6 53826 217969_at C11orf2 738218228_s_at TNKS2 80351 218723_s_at C13orf15 28984 218790_s_at TMLHE55217 218796_at FERMT1 55612 218862_at ASB13 79754 218935_at EHD3 30845218938_at FBXL15 79176 218952_at PCSK1N 27344 218976_at DNAJC12 56521219005_at TMEM59L 25789 219093_at PID1 55022 219107_at BCAN 63827219144_at DUSP26 78986 219170_at FSD1 79187 219196_at SCG3 29106219230_at TMEM100 55273 219273_at CCNK 8812 219305_x_at FBXO2 26232219370_at RPRM 56475 219415_at TTYH1 57348 219521_at B3GAT1 27087219537_x_at DLL3 10683 219732_at RP11- 54886 35N6.1 219743_at HEY2 23493219961_s_at C20orf19 55857 220005_at P2RY13 53829 220061_at ACSM5 54988220188_at JPH3 57338 221310_at FGF14 2259 221527_s_at PARD3 56288221552_at ABHD6 57406 221578_at RASSF4 83937 221623_at BCAN 63827221679_s_at ABHD6 57406 221792_at RAB6B 51560 221824_s_at 9-Mar 220972221861_at — — 221959_at FAM110B 90362 222171_s_at PKNOX2 63876222783_s_at SMOC1 64093 222784_at SMOC1 64093 222898_s_at DLL3 10683222957_at NEU4 129807 223315_at NTN4 59277 223552_at LRRC4 64101223614_at C8orf57 84257 223839_s_at SCD 6319 223865_at SOX6 55553223885_at CALN1 83698 224215_s_at DLL1 28514 224393_s_at CECR6 27439224482_s_at RAB11FIP4 84440 224763_at RPL37 6167 225379_at MAPT 4137225482_at KIF1A 547 226186_at TMOD2 29767 226587_at — — 226591_at — —226623_at PHYHIPL 84457 226680_at IKZF5 64376 226913_s_at SOX8 30812226918_at JPH4 84502 227202_at CNTN1 1272 227341_at C10orf30 222389227401_at IL17D 53342 227425_at REPS2 9185 227440_at ANKS1B 56899227498_at — — 227550_at LOC143381 143381 227769_at — — 227845_s_at SHD56961 227949_at PHACTR3 116154 227984_at LOC650392 650392 228017_s_atNKAIN4 128414 228018_at NKAIN4 128414 228051_at LOC202451 202451228165_at C12orf53 196500 228170_at OLIG1 116448 228193_s_at C13orf1528984 228206_at HS3ST4 9951 228376_at GGTA1 2681 228403_at C9orf165375704 228509_at SPHKAP 80309 228598_at DPP10 57628 228608_at NALCN259232 228679_at — — 229233_at NRG3 10718 229234_at ZC3H12B 340554229294_at JPH3 57338 229378_at STOX1 219736 229459_at FAM19A5 25817229463_at NTRK2 4915 229545_at FERMT1 55612 229590_at RPL13 6137229612_at — — 229613_at — — 229655_at FAM19A5 25817 229724_at GABRB32562 229799_s_at NCAM1 4684 229831_at CNTN3 5067 229875_at ZDHHC22283576 229901_at ZNF488 118738 229921_at — — 230287_at SGSM1 129049230307_at SLC25A21 89874 230336_at — — 230551_at KSR2 283455 230568_x_atDLL3 10683 230577_at — — 230771_at NKAIN4 128414 230869_at FAM155A728215 230932_at — — 230942_at CMTM5 116173 231103_at — — 231131_atFAM133A 286499 231214_at — — 231650_s_at SEZ6L 23544 231798_at NOG 9241231935_at ARPP-21 10777 231977_at GRID1 2894 231978_at TPCN2 219931231980_at — — 232010_at FSTL5 56884 232059_at DSCAML1 57453 232192_atLOC153811 153811 232195_at GPR158 57512 232833_at — — 233051_at SLITRK284631 234472_at GALNT13 114805 234996_at CALCRL 10203 235118_at — —235527_at DLGAP1 9229 235591_at SSTR1 6751 236038_at — — 236095_at NTRK24915 236287_at — — 236290_at DOK6 220164 236333_at — — 236433_at — —236536_at GALNT13 114805 236538_at GRIA2 2891 236576_at — — 236748_atRASGEF1C 255426 236771_at C6orf159 134701 237094_at FAM19A5 25817238458_at EFHA2 286097 238521_at — — 238603_at LOC254559 254559238663_x_at GRIA4 2893 239293_at NRSN1 140767 239509_at — — 239787_atKCTD4 386618 239827_at C13orf15 28984 240067_at — — 240218_at DSCAM 1826240228_at CSMD3 114788 240433_x_at — — 240512_x_at KCTD4 386618240578_at — — 240869_at — — 241255_at — — 241365_at — — 241729_at DOK6220164 241909_at TNKS2 80351 242571_at REPS2 9185 242651_at — —243526_at WDR86 349136 243779_at GALNT13 114805 243952_at psiTPTE22387590 244184_at — — 244218_at — — 244623_at KCNQ5 56479 35846_at THRA7067 43511_s_at — — 49111_at — — 60474_at FERMT1 55612 89977_at ACSM554988 91920_at BCAN 63827

TABLE 3C Table 3C. Genes in the PROGES signature. AffyID Gene SymbolGene ID 200934_at DEK 7913 201016_at EIF1AX 1964 201202_at PCNA 5111201291_s_at TOP2A 7153 201292_at TOP2A 7153 201477_s_at RRM1 6240201663_s_at SMC4 10051 201664_at SMC4 10051 201764_at TMEM106C 79022201890_at RRM2 6241 201930_at MCM6 4175 201970_s_at NASP 4678202107_s_at MCM2 4171 202276_at SHFM1 7979 202412_s_at USP1 7398202503_s_at KIAA0101 9768 202532_s_at DHFR 1719 202533_s_at DHFR 1719202534_x_at DHFR 1719 202589_at TYMS 7298 202904_s_at LSM5 23658202979_s_at CREBZF 58487 203046_s_at TIMELESS 8914 203213_at CDC2 983203276_at LMNB1 4001 203344_s_at RBBP8 5932 203347_s_at MTF2 22823203358_s_at EZH2 2146 203362_s_at MAD2L1 4085 203401_at PRPS2 5634203560_at GGH 8836 203675_at NUCB2 4925 203764_at DLGAP5 9787 203830_atC17orf75 64149 203925_at GCLM 2730 203960_s_at HSPB11 51668 203967_atCDC6 990 203968_s_at CDC6 990 203976_s_at CHAF1A 10036 204005_s_at PAWR5074 204023_at RFC4 5984 204026_s_at ZWINT 11130 204092_s_at AURKA 6790204146_at RAD51AP1 10635 204159_at CDKN2C 1031 204162_at NDC80 10403204170_s_at CKS2 1164 204240_s_at SMC2 10592 204244_s_at DBF4 10926204252_at CDK2 1017 204342_at SLC25A24 29957 204485_s_at TOM1L1 10040204517_at PPIC 5480 204531_s_at BRCA1 672 204641_at NEK2 4751204709_s_at KIF23 9493 204775_at CHAF1B 8208 204784_s_at MLF1 4291204822_at TTK 7272 204825_at MELK 9833 204833_at ATG12 9140 204886_atPLK4 10733 204947_at E2F1 1869 204962_s_at CENPA 1058 205023_at RAD515888 205034_at CCNE2 9134 205046_at CENPE 1062 205061_s_at EXOSC9 5393205063_at SIP1 8487 205071_x_at XRCC4 7518 205167_s_at CDC25C 995205176_s_at ITGB3BP 23421 205260_s_at ACYP1 97 205339_at STIL 6491205345_at BARD1 580 205393_s_at CHEK1 1111 205394_at CHEK1 1111205628_at PRIM2 5558 206102_at GINS1 9837 206172_at IL13RA2 3598206316_s_at KNTC1 9735 206364_at KIF14 9928 207039_at CDKN2A 1029207165_at HMMR 3161 208051_s_at PAIP1 10605 208079_s_at AURKA 6790208443_x_at SHOX2 6474 208808_s_at HMGB2 3148 208995_s_at PPIG 9360209172_s_at CENPF 1063 209507_at RPA3 6119 209642_at BUB1 699209644_x_at CDKN2A 1029 209709_s_at HMMR 3161 210093_s_at MAGOH 4116210691_s_at CACYBP 27101 211200_s_at EFCAB2 84288 211675_s_at MDFIC29969 211713_x_at KIAA0101 9768 211747_s_at LSM5 23658 212094_at PEG1023089 212533_at WEE1 7465 212918_at RECQL 5965 212949_at NCAPH 23397213007_at FANCI 55215 213017_at ABHD3 171586 213226_at CCNA2 890213253_at SMC2 10592 213353_at ABCA5 23461 213424_at KIAA0895 23366214224_s_at PIN4 5303 214431_at GMPS 8833 214710_s_at CCNB1 891214804_at CENPI 2491 216228_s_at WDHD1 11169 218349_s_at ZWILCH 55055218355_at KIF4A 24137 218585_s_at DTL 51514 218602_s_at FAM29A 54801218662_s_at NCAPG 64151 218663_at NCAPG 64151 218726_at HJURP 55355218772_x_at TMEM38B 55151 218875_s_at FBXO5 26271 218883_s_at MLF1IP79682 218894_s_at MAGOHB 55110 218911_at YEATS4 8089 218981_at ACN957001 219105_x_at ORC6L 23594 219174_at IFT74 80173 219208_at FBXO1180204 219288_at C3orf14 57415 219512_at DSN1 79980 219555_s_at CENPN55839 219587_at TTC12 54970 219650_at ERCC6L 54821 219703_at MNS1 55329219736_at TRIM36 55521 219758_at TTC26 79989 219787_s_at ECT2 1894219918_s_at ASPM 259266 219978_s_at NUSAP1 51203 219990_at E2F8 79733220060_s_at C12orf48 55010 220144_s_at ANKRD5 63926 220175_s_at CBWD155871 220840_s_at C1orf112 55732 221258_s_at KIF18A 81930 221521_s_atGINS2 51659 221677_s_at DONSON 29980 222606_at ZWILCH 55055 222768_s_atTRMT6 51605 222848_at CENPK 64105 223133_at TMEM14B 81853 223274_atTCF19 6941 223381_at NUF2 83540 223542_at ANKRD32 84250 223544_at TMEM7984283 223700_at MND1 84057 224204_x_at ARNTL2 56938 224428_s_at CDCA783879 224443_at C1orf97 84791 224444_s_at C1orf97 84791 224715_at WDR3489891 224944_at TMPO 7112 225078_at EMP2 2013 225297_at CCDC5 115106226117_at TIFA 92610 226223_at PAWR 5074 226231_at PAWR 5074 226287_atCCDC34 91057 226452_at PDK1 5163 226908_at LRIG3 121227 226936_atC6orf173 387103 227314_at ITGA2 3673 227350_at HELLS 3070 227793_at — —228033_at E2F7 144455 228280_at ZC3HAV1L 92092 228654_at SPIN4 139886228729_at CCNB1 891 228776_at GJC1 10052 229305_at MLF1IP 79682229490_s_at — — 229551_x_at ZNF367 195828 229974_at EVC2 132884230121_at C1orf133 574036 230165_at SGOL2 151246 230696_at — — 230860_atC3orf34 84984 232065_x_at CENPL 91687 232242_at — — 233970_s_at TRMT651605 234863_x_at FBXO5 26271 235004_at RBM24 221662 235113_at PPIL5122769 235425_at SGOL2 151246 235572_at SPC24 147841 235609_at — —235644_at CCDC138 165055 235949_at — — 236222_at C3orf15 89876 236641_atKIF14 9928 236915_at C4orf47 441054 237469_at TOP2A 7153 237585_atC4orf47 441054 238021_s_at hCG_1815491 643911 238022_at hCG_1815491643911 238075_at — — 238843_at NPHP1 4867 238865_at PABPC4L 132430239413_at CEP152 22995 239680_at — — 241705_at ABCA5 23461 242560_atFANCD2 2177 243198_at TEX9 374618 48808_at DHFR 1719

TABLE 4 List of 928 Transcription Factors used by the MRA analysis. No.TF Name 1 AATF 2 ADNP 3 AEBP1 4 AFF1 5 AFF3 6 AFF4 7 AHCTF1 8 AHR 9 ALX410 AR 11 ARID3A 12 ARID4A 13 ARNT 14 ARNT2 15 ARNTL 16 ARNTL2 17 ASCL118 ASCL2 19 ATBF1 20 ATF1 21 ATF2 22 ATF3 23 ATF4 24 ATF5 25 ATF6 26ATF7 27 ATOH1 28 BACH1 29 BACH2 30 BAPX1 31 BARX2 32 BATF 33 BAZ1B 34BCL6 35 BHLHB2 36 BHLHB3 37 BLZF1 38 BNC1 39 BRD8 40 BRF1 41 BRPF1 42BTAF1 43 BUD31 44 C2orf3 45 CBFA2T2 46 CBFA2T3 47 CBFB 48 CBL 49 CCRN4L50 CDX1 51 CDX2 52 CDX4 53 CEBPA 54 CEBPB 55 CEBPD 56 CEBPE 57 CEBPG 58CEBPZ 59 CHES1 60 CIITA 61 CIR 62 CITED1 63 CITED2 64 CLOCK 65 CNBP 66CNOT7 67 CNOT8 68 CREB1 69 CREB3 70 CREB3L1 71 CREB3L2 72 CREB5 73CREBBP 74 CREBL1 75 CREBL2 76 CREG1 77 CREM 78 CRX 79 CSDA 80 CTBP1 81CTBP2 82 CTCF 83 CTNNB1 84 CUTL1 85 CUTL2 86 DAXX 87 DBP 88 DDIT3 89 DEK90 DENND4A 91 DLX2 92 DLX4 93 DLX5 94 DLX6 95 DMTF1 96 DR1 97 DRAP1 98DSCR1 99 DUX1 100 E2F1 101 E2F2 102 E2F3 103 E2F4 104 E2F5 105 E2F6 106E2F8 107 E4F1 108 EDF1 109 EGR1 110 EGR2 111 EGR3 112 EGR4 113 ELF1 114ELF2 115 ELF3 116 ELF4 117 ELF5 118 ELK1 119 ELK3 120 ELK4 121 EMX1 122EMX2 123 EN1 124 EN2 125 ENO1 126 EP300 127 EPAS1 128 ERCC6 129 ERF 130ERG 131 ESR1 132 ESR2 133 ESRRA 134 ESRRB 135 ESRRG 136 ETS1 137 ETS2138 ETV1 139 ETV3 140 ETV4 141 ETV5 142 ETV6 143 ETV7 144 EVI1 145 EVX1146 EWSR1 147 FALZ 148 FEV 149 FEZF2 150 FLI1 151 FMNL2 152 FOS 153 FOSB154 FOSL1 155 FOSL2 156 FOXA1 157 FOXA2 158 FOXB1 159 FOXD1 160 FOXD3161 FOXE1 162 FOXE3 163 FOXF1 164 FOXF2 165 FOXG1B 166 FOXH1 167 FOXI1168 FOXJ1 169 FOXJ2 170 FOXJ3 171 FOXK2 172 FOXL1 173 FOXM1 174 FOXN1175 FOXO1A 176 FOXO3A 177 FOXP1 178 FOXP3 179 FUBP1 180 FUBP3 181 GABPB2182 GAS7 183 GATA1 184 GATA2 185 GATA3 186 GATA4 187 GATA6 188 GATAD1189 GATAD2A 190 GBX2 191 GLI2 192 GLI3 193 GMEB1 194 GRLF1 195 GTF2IRD1196 HAND1 197 HAND2 198 HBP1 199 HCFC1 200 HCLS1 201 HES1 202 HES2 203HESX1 204 HEY1 205 HEY2 206 HEYL 207 HHEX 208 HIC1 209 HIF1A 210 HIF3A211 HIRA 212 HIVEP1 213 HIVEP2 214 HIVEP3 215 HKR3 216 HLF 217 HLX1 218HLXB9 219 HMBOX1 220 HMG20A 221 HMG20B 222 HMGA1 223 HMGA2 224 HMGB1 225HMGB2 226 HMX1 227 HNF4A 228 HNF4G 229 HOP 230 HOXA1 231 HOXA10 232HOXA11 233 HOXA2 234 HOXA3 235 HOXA4 236 HOXA5 237 HOXA6 238 HOXA7 239HOXA9 240 HOXB13 241 HOXB2 242 HOXB5 243 HOXB6 244 HOXB7 245 HOXB8 246HOXB9 247 HOXC10 248 HOXC11 249 HOXC4 250 HOXC5 251 HOXC6 252 HOXD1 253HOXD10 254 HOXD11 255 HOXD12 256 HOXD13 257 HOXD3 258 HOXD4 259 HOXD9260 H-plk 261 HR 262 HSF1 263 HSF2 264 HSF4 265 HTLF 266 IKZF1 267 IKZF4268 IKZF5 269 ILF2 270 INSM1 271 IPF1 272 IRF1 273 IRF2 274 IRF3 275IRF4 276 IRF5 277 IRF6 278 IRF7 279 IRF8 280 IRX4 281 IRX5 282 ISGF3G283 ISL1 284 JARID1A 285 JARID1B 286 JUN 287 JUNB 288 JUND 289 KIAA0415290 KIAA0963 291 KLF1 292 KLF10 293 KLF11 294 KLF12 295 KLF13 296 KLF15297 KLF2 298 KLF3 299 KLF4 300 KLF5 301 KLF6 302 KLF7 303 KLF9 304 KNTC1305 L3MBTL 306 LASS2 307 LASS4 308 LASS6 309 LBX1 310 LHX2 311 LHX3 312LHX5 313 LHX6 314 LMO1 315 LMO4 316 LMX1B 317 LOC645682 318 LYL1 319LZTFL1 320 LZTR1 321 LZTS1 322 MAF 323 MAFB 324 MAFF 325 MAFG 326 MAFK327 MAML3 328 MAX 329 MAZ 330 MBD1 331 MDS1 332 MECP2 333 MEF2A 334MEF2B 335 MEF2C 336 MEF2D 337 MEIS1 338 MEIS2 339 MEIS3P1 340 MEOX1 341MEOX2 342 MGA 343 MITF 344 MIZF 345 MLL 346 MLL4 347 MLLT10 348 MLLT7349 MLX 350 MLXIP 351 MLXIPL 352 MNT 353 MSC 354 MSL3L1 355 MSRB2 356MSX1 357 MSX2 358 MTA1 359 MTA2 360 MTF1 361 MXD1 362 MYB 363 MYBL1 364MYBL2 365 MYC 366 MYCL1 367 MYCN 368 MYF6 369 MYNN 370 MYOD1 371 MYOG372 MYST2 373 MYT1 374 MYT1L 375 MZF1 376 NANOG 377 NCOR1 378 NEUROD1379 NEUROD2 380 NEUROG1 381 NEUROG3 382 NFAT5 383 NFATC1 384 NFATC3 385NFATC4 386 NFE2 387 NFE2L1 388 NFE2L2 389 NFE2L3 390 NFIB 391 NFIC 392NFIL3 393 NFIX 394 NFKB1 395 NFKB2 396 NFRKB 397 NFX1 398 NFYA 399 NFYB400 NFYC 401 NHLH1 402 NHLH2 403 NKRF 404 NKX2-2 405 NKX2-5 406 NKX2-8407 NKX3-1 408 NKX6-1 409 NOTCH2 410 NPAS2 411 NPAS3 412 NPAT 413 NR0B1414 NR0B2 415 NR1D2 416 NR1H2 417 NR1H3 418 NR1H4 419 NR1I2 420 NR1I3421 NR2C1 422 NR2C2 423 NR2E1 424 NR2E3 425 NR2F1 426 NR2F2 427 NR2F6428 NR3C1 429 NR3C2 430 NR4A1 431 NR4A2 432 NR4A3 433 NR5A1 434 NR5A2435 NR6A1 436 NRF1 437 NRL 438 OLIG2 439 ONECUT1 440 OVOL1 441 PAX1 442PAX2 443 PAX3 444 PAX4 445 PAX6 446 PAX7 447 PAX8 448 PAX9 449 PBX1 450PBX2 451 PBX3 452 PCGF2 453 PEG3 454 PFDN1 455 PGR 456 PHF2 457 PHOX2A458 PHOX2B 459 PHTF1 460 PHTF2 461 PITX1 462 PITX3 463 PKNOX1 464 PKNOX2465 PLAG1 466 PLAGL1 467 PLAGL2 468 PML 469 POU2F1 470 POU2F2 471 POU2F3472 POU3F1 473 POU3F2 474 POU3F3 475 POU3F4 476 POU4F1 477 POU4F2 478POU6F1 479 POU6F2 480 PPARA 481 PPARD 482 PPARG 483 PRDM1 484 PRDM16 485PRDM2 486 PREB 487 PROP1 488 PRRX1 489 PRRX2 490 PTTG1 491 PURA 492 RARA493 RARB 494 RARG 495 RAX 496 RB1 497 RBL2 498 RBPSUH 499 RBPSUHL 500REL 501 RELA 502 RELB 503 RERE 504 REST 505 REXO4 506 RFX1 507 RFX2 508RFX3 509 RFX5 510 RFXANK 511 RFXAP 512 RLF 513 RNF4 514 RORA 515 RORB516 RORC 517 RREB1 518 RUNX1 519 RUNX1T1 520 RUNX2 521 RUNX3 522 RXRA523 RXRB 524 RXRG 525 SALL1 526 SALL2 527 SATB1 528 SATB2 529 SCAND1 530SCAND2 531 SCML1 532 SCML2 533 SHOX 534 SHOX2 535 SIM2 536 SIX1 537 SIX2538 SIX3 539 SIX5 540 SIX6 541 SLC26A3 542 SLC2A4RG 543 SLC30A9 544SMAD1 545 SMAD2 546 SMAD3 547 SMAD4 548 SMAD5 549 SMAD6 550 SMAD7 551SMAD9 552 SMARCA3 553 SMARCA4 554 SNAI1 555 SNAI2 556 SNAPC2 557 SNAPC4558 SNAPC5 559 SNFT 560 SOLH 561 SOX1 562 SOX10 563 SOX11 564 SOX12 565SOX13 566 SOX15 567 SOX17 568 SOX18 569 SOX2 570 SOX21 571 SOX3 572 SOX4573 SOX5 574 SOX9 575 SP1 576 SP140 577 SP2 578 SP3 579 SP4 580 SPDEF581 SPI1 582 SPIB 583 SREBF1 584 SREBF2 585 SRF 586 ST18 587 STAT1 588STAT2 589 STAT3 590 STAT4 591 STAT5A 592 STAT5B 593 STAT6 594 SUPT4H1595 SUPT6H 596 T 597 TADA2L 598 TADA3L 599 TAF1B 600 TAF5L 601 TAL1 602TARDBP 603 TBR1 604 TBX1 605 TBX10 606 TBX19 607 TBX2 608 TBX21 609 TBX3610 TBX4 611 TBX5 612 TBX6 613 TCEAL1 614 TCF1 615 TCF12 616 TCF15 617TCF2 618 TCF21 619 TCF25 620 TCF3 621 TCF4 622 TCF7 623 TCF7L1 624TCF7L2 625 TCF8 626 TCFL5 627 TEAD1 628 TEAD3 629 TEAD4 630 TEF 631 TFAM632 TFAP2A 633 TFAP2B 634 TFAP2C 635 TFAP4 636 TFCP2 637 TFCP2L1 638TFDP1 639 TFDP2 640 TFDP3 641 TFE3 642 TFEB 643 TFEC 644 TGIF 645 TGIF2646 THRA 647 THRB 648 TLX1 649 TLX2 650 TNRC4 651 TP53 652 TP73 653TP73L 654 TRERF1 655 TRIM22 656 TRIM25 657 TRIM28 658 TRIM29 659 TRPS1660 TSC22D1 661 TSC22D2 662 TSC22D3 663 TSC22D4 664 TULP4 665 TWIST1 666UBN1 667 UBP1 668 USF2 669 VAV1 670 VAX2 671 VDR 672 VENTX 673 VEZF1 674VPS72 675 VSX1 676 WT1 677 XBP1 678 YBX1 679 YEATS4 680 YWHAE 681 YWHAZ682 YY1 683 YY2 684 ZBTB16 685 ZBTB17 686 ZBTB22 687 ZBTB25 688 ZBTB38689 ZBTB43 690 ZBTB6 691 ZBTB7A 692 ZBTB7B 693 ZF 694 ZFHX1B 695 ZFHX4696 ZFP36L1 697 ZFP36L2 698 ZFP37 699 ZFP95 700 ZFX 701 ZFY 702 ZHX2 703ZHX3 704 ZIC1 705 ZIM2 706 ZKSCAN1 707 ZMYM2 708 ZMYM3 709 ZMYM4 710ZNF10 711 ZNF117 712 ZNF12 713 ZNF124 714 ZNF131 715 ZNF132 716 ZNF133717 ZNF134 718 ZNF135 719 ZNF136 720 ZNF137 721 ZNF14 722 ZNF140 723ZNF141 724 ZNF142 725 ZNF143 726 ZNF146 727 ZNF148 728 ZNF154 729 ZNF155730 ZNF16 731 ZNF160 732 ZNF167 733 ZNF174 734 ZNF175 735 ZNF177 736ZNF180 737 ZNF184 738 ZNF185 739 ZNF187 740 ZNF189 741 ZNF192 742 ZNF193743 ZNF195 744 ZNF197 745 ZNF20 746 ZNF200 747 ZNF202 748 ZNF204 749ZNF205 750 ZNF207 751 ZNF211 752 ZNF212 753 ZNF215 754 ZNF217 755 ZNF219756 ZNF22 757 ZNF221 758 ZNF222 759 ZNF223 760 ZNF224 761 ZNF225 762ZNF226 763 ZNF227 764 ZNF228 765 ZNF230 766 ZNF232 767 ZNF235 768 ZNF236769 ZNF238 770 ZNF239 771 ZNF24 772 ZNF248 773 ZNF250 774 ZNF253 775ZNF259 776 ZNF26 777 ZNF263 778 ZNF264 779 ZNF266 780 ZNF267 781 ZNF268782 ZNF271 783 ZNF273 784 ZNF274 785 ZNF277 786 ZNF278 787 ZNF281 788ZNF282 789 ZNF286 790 ZNF287 791 ZNF289 792 ZNF291 793 ZNF292 794 ZNF294795 ZNF3 796 ZNF302 797 ZNF304 798 ZNF306 799 ZNF307 800 ZNF313 801ZNF318 802 ZNF32 803 ZNF322B 804 ZNF323 805 ZNF324 806 ZNF329 807 ZNF330808 ZNF331 809 ZNF334 810 ZNF335 811 ZNF337 812 ZNF33B 813 ZNF34 814ZNF343 815 ZNF345 816 ZNF35 817 ZNF350 818 ZNF354A 819 ZNF358 820 ZNF364821 ZNF365 822 ZNF384 823 ZNF394 824 ZNF395 825 ZNF403 826 ZNF407 827ZNF408 828 ZNF409 829 ZNF410 830 ZNF415 831 ZNF419A 832 ZNF42 833 ZNF423834 ZNF426 835 ZNF43 836 ZNF430 837 ZNF432 838 ZNF434 839 ZNF435 840ZNF44 841 ZNF440 842 ZNF443 843 ZNF444 844 ZNF446 845 ZNF447 846 ZNF45847 ZNF451 848 ZNF460 849 ZNF467 850 ZNF468 851 ZNF471 852 ZNF473 853ZNF480 854 ZNF484 855 ZNF493 856 ZNF500 857 ZNF506 858 ZNF507 859 ZNF508860 ZNF510 861 ZNF516 862 ZNF518 863 ZNF528 864 ZNF529 865 ZNF532 866ZNF536 867 ZNF544 868 ZNF549 869 ZNF550 870 ZNF551 871 ZNF552 872 ZNF556873 ZNF557 874 ZNF562 875 ZNF573 876 ZNF574 877 ZNF576 878 ZNF580 879ZNF586 880 ZNF587 881 ZNF588 882 ZNF589 883 ZNF592 884 ZNF593 885 ZNF606886 ZNF609 887 ZNF611 888 ZNF614 889 ZNF623 890 ZNF629 891 ZNF638 892ZNF643 893 ZNF646 894 ZNF652 895 ZNF654 896 ZNF659 897 ZNF665 898 ZNF667899 ZNF668 900 ZNF669 901 ZNF671 902 ZNF672 903 ZNF673 904 ZNF675 905ZNF682 906 ZNF688 907 ZNF692 908 ZNF695 909 ZNF696 910 ZNF7 911 ZNF701912 ZNF702 913 ZNF706 914 ZNF710 915 ZNF711 916 ZNF74 917 ZNF75 918ZNF79 919 ZNF8 920 ZNF81 921 ZNF83 922 ZNF84 923 ZNF85 924 ZNF91 925ZNF93 926 ZNF96 927 ZNFN1A1 928 ZSCAN5

TABLE 5 Ranked list of the TFs most frequently connected to the MGESpredicted by ARACNe and the TFs with consensus enrichment in MGESpromoters. TFs marked in blue are MRA-inferred TFs with significantenrichment of binding site in MGES promoters, and TFs marked in pink areenriched in DNA binding and highly connected to MGES in the ARACNeinferred networks. (a) MRA (b) DNA-Binding

TABLE 6 Table 6. Regulon overlap analysis. The proportion of targetgenes shared by pairs of TFs is significantly higher than expected bychance. The top-right portion of the table shows the odds ratio and thebottom-left portion the FET p-value for the contingency table of thenumber of target genes specific and shared by each TF among all genestested by ARACNe as potential targets. Stat3 C/EBPβ FosL2 bHLH-B2 Runx1Stat3 4.81 10.6 9.39 6.29 C/EBPβ 1.77E−09 13.9 6.63 13.5 FosL2 3.00E−464.12E−40 13.6 12.3 bHLH-B2 2.15E−25 4.76E−17 1.76E−41 5.87 Runx19.56E−28 1.78E−44 2.26E−68 8.45E−17

TABLE 7 Master Regulators inferred by the MRA and SLR algorithms usingthe MGES signature..

TABLE 8 Table 8. TFs with more than 20 connections with MGES, PNGES andPROGES in the transcriptional networks. TFs marked in red control morethan one signature. MGES Analysis TF MRA-rank Overlap p-value SLR-rankLR-Coeff FOSL2 1 45 9.4E−39 5 0.21 ZNF238 2 37 9.6E−28 2 −0.34 RUNX1 337 2.3E−24 4 0.13 C/EBP(*) 4 30 3.2E−19 1 0.40 C/EBPδ 5 27 1.2E−19 60.42 STAT3 6 26 1.2E−16 7 0.40 BHLHB2 7 25 7.8E−21 9 0.41 MYCN 8 256.2E−20 37 −0.11 FOSL1 9 23 3.6E−25 47 0.24 ELF4 10 21 7.0E−09 34 0.1C/EBPβ 11 20 2.2E−15 28 0.35 LZTS1 12 20 3.8E−14 3 0.22 TBX2 13 174.6E−12 23 0.17 SATB1 14 17 1.4E−07 21 −0.32 IRF1 15 16 2.0E−11 19 0.48EPAS1 16 16 2.6E−09 16 0.21 NFIB 17 15 5.4E−07 8 −0.32 KLF6 18 142.0E−11 — 0.16 NFYB 19 14 3.5E−07 14 −0.55 ELK3 20 14 1.8E−06 53 0.24‘—’ indicate that TF is not significant in regulon enrichment analysisand not included in SLR analysis (*)The C/EBP metagene includes targetsof both C/EBPβ and C/EBPδ

TABLE 9 shRNA mediated knock-down of MR-TFs in human glioma cells. a,Enrichment of each MR-TF regulon on each TF-knock-down gene expressionprofile by GSEA. Five additional TFs showing similar regulon size wereadded to the analysis as negative controls: ATF2 for Stat3, SOX15 forC/EBPβ, ZNF500 for FosL2 and Runx1, and ZNF277 for bHLH-B2. b,Enrichment of the MGES on genes downregulated after each MR-TFknock-down. Shown is the normalized enrichment score (nES) and p-valueestimated by permuting genes. Table 9a Silencing C/EBPβ Stat3 FosL2bHLH-B2 Runx1 Regulon Size nES p-value nES p-value nES p-value nESp-value nES p-value Module TFs Stat3 366 2.49 0.0077 1.78 0.0397 3.290.0011 3.04 0.0016 2.18 0.0146 C/EBPβ 209 1.91 0.0306 2.43 0.0092 2.300.0121 1.66 0.0539 3.62 0.0001 FosL2 403 3.83 0.0001 4.98 <1E−4 3.830.0001 3.39 0.0007 3.66 0.0001 bHLH-B2 226 1.74 0.0429 0.59 0.2773 2.170.0171 1.39 0.0870 3.09 0.0014 Runx1 490 0.55 0.2910 2.41 0.0097 1.180.1267 1.98 0.0274 2.13 0.0168 Control TFs ATF2 386 1.54 0.0615 −0.240.5965 1.42 0.0865 0.20 0.4134 −1.49 0.9293 SOX15 213 0.28 0.3908 −2.810.9976 −0.12 0.5496 −0.28 0.6070 0.70 0.2397 ZNF500 469 −0.24 0.59700.20 0.4185 0.85 0.2012 −0.74 0.7698 0.11 0.4543 ZNF277 238 −0.79 0.7852−0.55 0.7116 0.41 0.3433 0.90 0.1849 −0.91 0.8162 Table 9b C/EBPβ Stat3FosL2 bHLH-B2 Runx1 Silencing nES p value nES p value nES p value nES pvalue nES p value MGES Enrich. 3.23 0.0001 3.59 0.0001 3.92 <1E−4 4.364.67E−06 3.82 <1E−4

TABLE 10 Table 10. mRNA levels for C/EBPβ and Stat3 after silencing andover- expression experiments. Shown is the median ± MAD and U-testp-value for the C/EBPβ and Stat3 mRNA levels relative to non-targetshRNA transduced cells and mRNA levels for the GAPDH mRNA housekeepinggene. C/EBPβ mRNA Stat3 mRNA Median ± Median ± MAD p-value MAD p-valueSi- C/EBPβ 0.26 ± 0.119 0.00108 1.13 ± 0.43  0.153 lencing Stat3 0.87 ±0.111 0.149 0.205 ± 0.052  0.00109 C/EBPβ × 0.25 ± 0.163 0.00165  0.2 ±0.074 0.00165 Stat3 Over- C/EBPβ 45.53 ± 23.929 0.00781 0.89 ± 0.3930.383 ex- Stat3 1.17 ± 0.541 0.313 3.79 ± 2.758 0.00781 pression CEBPβ ×155.1 ± 57.11  0.00391 2.79 ± 1.171 0.00391 Stat3

TABLE 11 Table 11. GSEA of ARACNe regulons on the gene expressionprofile rank- sorted by its correlation with the mRNA levels of C/EBPβ,Stat3, and C/EBPβ × Stat3 (the metagene). Shown is the regulon size,normalized enrichment score (nES), sample permutation-based p-value andleading-edge odds ratio (LEOR) for the MR-TFs: C/EBPβ, Stat3, FosL2,bHLH-B2 and Runx1; and 5 randomly selected control TFs with comparablenumber of target genes. C/EBPβ mRNA Stat3 mRNA C/EBPβ × Stat3 nESp-value LEOR nES p-value LEOR nES p-value LEOR C/EBPβ 2.05 0.0290 2.293.17 0.0008 3.46 2.67 0.0038 2.75 Stat3 1.91 0.0340 1.94 3.21 0.00072.38 2.60 0.0046 2.56 FosL2 2.03 0.0210 2.35 3.60 0.0002 3.51 3.020.0013 3.26 bHLH- 2.07 0.0190 2.37 3.48 0.0002 3.28 2.82 0.0024 2.91 B2Runx1 2.16 0.0170 1.81 4.04 <1E−4 2.56 3.24 0.0006 2.25 ATF2 −1.370.8800 — −1.43 0.9220 — −1.57 0.9290 — SOX15 0.15 0.4370 — 0.36 0.3850 —0.42 0.3460 — ZNF500 −1.50 0.9190 — −0.77 0.7530 — −1.34 0.8990 — ZNF277−0.29 0.6060 — 0.56 0.3120 — 0.20 0.4250 —

TABLE 12 List of 884 genes in TCGA Worst Prognosis Signature (TWPS),identified by differential expression analysis (p < 0.05 based onStudent's t-test) between 77 low- and 21 high-survival samples in theTCGA dataset. Rank Gene ID p-value Overlap 1 IL8 1.1E−06 2 PTX3 2.8E−063 EFEMP2 5.9E−06 1 4 SSR3 6.7E−06 5 TAGLN2 6.8E−06 6 PDPN 1.7E−05 1 7EMP3 2.7E−05 1 8 TFRC 2.9E−05 9 GLT8D1 5.2E−05 10 PSMD13 5.3E−05 11 ADM5.8E−05 12 LGALS8 6.6E−05 13 PLOD2 7.3E−05 14 CHI3L1 7.4E−05 1 15 TMEM228.0E−05 16 NRN1 8.5E−05 17 LGALS1 8.7E−05 18 RIG 9.0E−05 19 IGFBP29.6E−05 20 C6orf62 1.0E−04 21 MT1M 1.2E−04 22 LDHA 1.4E−04 23 NOL31.5E−04 24 TIMP1 1.6E−04 1 25 SCG2 1.7E−04 26 CLIC1 1.7E−04 27 ARFIP21.7E−04 28 HFE 2.0E−04 29 COPB1 2.2E−04 30 MDK 2.5E−04 31 DUSP6 2.5E−0432 NSUN5C 2.7E−04 33 KRT10 2.9E−04 34 PGK1 3.0E−04 35 DKK3 3.2E−04 36POLR1D 3.2E−04 37 FAS 3.4E−04 38 PCNP 3.6E−04 39 NSUN5 4.2E−04 40 DYNLT34.2E−04 41 TUBB2A 4.4E−04 42 UPP1 4.4E−04 43 ABHD3 4.5E−04 44 SPP15.0E−04 45 DDIT3 5.1E−04 46 NNMT 5.2E−04 47 SPA17 5.5E−04 48 SSBP25.5E−04 49 DLAT 5.6E−04 50 DRG2 5.6E−04 51 FAM3C 5.8E−04 52 ATP2B16.4E−04 53 DNAJB9 6.4E−04 54 ARNTL 6.4E−04 55 CD63 6.4E−04 56 MT1F6.6E−04 57 FLJ11286 6.8E−04 58 SDC2 6.8E−04 59 RAB33B 7.5E−04 60 PIGB7.7E−04 61 DERA 7.9E−04 62 PEX7 8.1E−04 63 RIOK3 8.2E−04 64 KIAA04098.4E−04 65 HRASLS3 9.5E−04 66 TAF9 9.5E−04 67 FZD7 9.5E−04 68 SLC25A249.6E−04 69 TRIP4 9.6E−04 70 FRAG1 9.6E−04 71 CARS 9.7E−04 72 EGLN39.7E−04 73 FAHD2A 9.9E−04 74 ANGPT1 1.0E−03 75 FLJ11506 1.0E−03 76 CD441.0E−03 77 GBE1 1.0E−03 78 NTAN1 1.0E−03 79 SLC35A3 1.0E−03 80 LOC3909401.1E−03 81 REXO2 1.1E−03 82 FLNC 1.1E−03 83 RPL23AP7 1.1E−03 84 FABP31.1E−03 85 AACS 1.2E−03 86 SLC38A6 1.2E−03 87 PTS 1.2E−03 88 SLC43A31.2E−03 89 HRH4 1.2E−03 90 TRIB3 1.2E−03 91 AP3S1 1.2E−03 92 C13orf181.2E−03 93 COQ10B 1.2E−03 94 RGN 1.2E−03 95 GTF2H5 1.3E−03 96 NUP1601.4E−03 97 DDX47 1.4E−03 98 LSM5 1.5E−03 99 TPI1 1.5E−03 100 KIAA04951.5E−03 101 S100A13 1.6E−03 102 ARTS-1 1.6E−03 103 CYCS 1.6E−03 104TMEM158 1.6E−03 105 IL1RAPL2 1.7E−03 106 HEMK1 1.7E−03 107 C3orf601.7E−03 108 NUP98 1.8E−03 109 TMBIM1 1.8E−03 110 HIGD1A 1.8E−03 111 SSH31.9E−03 112 MDS032 1.9E−03 113 EIF1 1.9E−03 114 DALRD3 1.9E−03 115 SYPL12.0E−03 116 APOE 2.0E−03 117 PTPN12 2.0E−03 118 TOM1L1 2.0E−03 119EIF4E2 2.0E−03 120 C7orf25 2.0E−03 121 KIAA0895 2.0E−03 122 HEBP12.1E−03 123 ECHDC2 2.1E−03 124 IQCG 2.2E−03 125 FKBP9 2.2E−03 126 SOD22.3E−03 127 RBP1 2.3E−03 128 MRPL17 2.3E−03 129 SLC2A3 2.3E−03 130 DUS4L2.5E−03 131 CCDC109B 2.5E−03 132 C12orf29 2.6E−03 133 FBXO17 2.6E−03 134CAMK2N1 2.6E−03 135 RIC8A 2.6E−03 136 HK2 2.6E−03 137 PLSCR1 2.7E−03 138G0S2 2.7E−03 139 DCTD 2.8E−03 140 SDHD 2.8E−03 141 MT1E 2.8E−03 142POLR2L 2.8E−03 143 OSTM1 2.8E−03 144 F3 2.9E−03 145 RNH1 2.9E−03 146CCL20 2.9E−03 147 CSRP1 2.9E−03 148 FLJ22222 2.9E−03 149 PDLIM3 2.9E−03150 ATG12 3.0E−03 151 COG5 3.0E−03 152 CBR1 3.1E−03 153 MTRR 3.2E−03 154MAFF 3.3E−03 155 LIN7C 3.3E−03 156 SPRY2 3.4E−03 157 BCL2A1 3.4E−03 158BCAP29 3.4E−03 159 STEAP3 3.4E−03 1 160 CTNNB1 3.4E−03 161 CYP3A433.5E−03 162 SMS 3.6E−03 163 GRPEL1 3.6E−03 164 DOK3 3.6E−03 165 CCL23.6E−03 166 ARSJ 3.6E−03 167 ITGA7 3.6E−03 1 168 FKBP2 3.7E−03 169 WWTR13.7E−03 170 PGCP 3.7E−03 171 VLDLR 3.7E−03 172 STK19 3.8E−03 173LOC201229 3.8E−03 174 TFPI 3.8E−03 175 POP5 3.8E−03 176 GAP43 3.8E−03177 FAM62A 3.8E−03 178 MT1G 3.8E−03 179 TUSC2 3.9E−03 180 MET 3.9E−03181 EPS8 3.9E−03 182 C19orf10 4.0E−03 183 ATP13A3 4.1E−03 184 UNC84A4.1E−03 185 GRB10 4.1E−03 186 STK17A 4.1E−03 187 RQCD1 4.2E−03 188C19orf53 4.3E−03 189 EXOC3 4.3E−03 190 HSD17B12 4.3E−03 191 PDGFA4.3E−03 1 192 RPL14 4.4E−03 193 HES1 4.4E−03 194 TMEM41B 4.4E−03 195SYNJ2 4.5E−03 196 TRAM1 4.5E−03 197 RCP9 4.5E−03 198 SP100 4.6E−03 199TNFRSF12A 4.6E−03 200 VAMP4 4.6E−03 201 CDC5L 4.7E−03 202 CHL1 4.7E−03203 ANGPTL4 4.8E−03 1 204 TNPO1 4.8E−03 205 TCEB1 4.8E−03 206 HBXIP4.9E−03 207 DNPEP 4.9E−03 208 ACOX2 4.9E−03 209 TNFAIP6 4.9E−03 210ARL4C 5.0E−03 211 FAM18B 5.0E−03 212 LITAF 5.1E−03 213 PMP22 5.2E−03 214ADFP 5.2E−03 215 RRAS2 5.2E−03 216 TSPAN13 5.2E−03 217 TIPARP 5.3E−03218 ARPC3 5.3E−03 219 NUP37 5.3E−03 220 TBCA 5.3E−03 221 S100A4 5.3E−03222 NSUN5B 5.3E−03 223 GOLT1B 5.3E−03 224 UGCG 5.3E−03 225 HMBS 5.3E−03226 ISG20 5.3E−03 227 IFT57 5.3E−03 228 CALR 5.5E−03 229 TBCE 5.5E−03230 MEOX2 5.5E−03 231 CSRP2 5.5E−03 232 PDIA4 5.6E−03 233 SMEK2 5.6E−03234 OBSL1 5.7E−03 235 CD164 5.7E−03 236 PRPS2 5.7E−03 237 PTDSS2 5.8E−03238 SPAG4 5.8E−03 239 RBPMS 5.8E−03 240 FN3KRP 5.9E−03 241 MXRA7 5.9E−03242 HEXB 6.0E−03 243 MGC14376 6.0E−03 244 ATP5L 6.0E−03 245 TMEM38B6.0E−03 246 GRB14 6.1E−03 247 BUD31 6.1E−03 248 NDP 6.1E−03 249 GCA6.1E−03 250 CLN5 6.2E−03 251 ASB4 6.2E−03 252 TSPAN4 6.2E−03 253 S100A66.2E−03 254 ILK 6.2E−03 255 GNG12 6.2E−03 256 BRP44L 6.4E−03 257 ABCB96.4E−03 258 MRPL49 6.4E−03 259 RNF14 6.4E−03 260 ARL8B 6.4E−03 261 TBL26.4E−03 262 NXPH4 6.5E−03 263 CYP3A7 6.5E−03 264 CHCHD2 6.6E−03 265LECT1 6.7E−03 266 SLC2A1 6.7E−03 267 COPS2 6.7E−03 268 ARF6 6.7E−03 269MAOB 6.7E−03 270 SMYD2 6.8E−03 271 SLC2A10 6.8E−03 272 CD58 6.8E−03 273C19orf42 6.8E−03 274 IL1RAP 6.9E−03 275 MPV17 6.9E−03 276 NPY2R 6.9E−03277 TIMM10 7.0E−03 278 PIPOX 7.1E−03 279 PUS7 7.3E−03 280 ORMDL2 7.3E−03281 HOXC6 7.3E−03 282 MAB21L2 7.3E−03 283 TM2D1 7.3E−03 284 GNAT37.3E−03 285 HOMER1 7.4E−03 286 C5orf21 7.5E−03 287 AP1S1 7.5E−03 288TCTA 7.6E−03 289 TRIM5 7.6E−03 290 UQCRQ 7.6E−03 291 ACTL6A 7.7E−03 292MYD88 7.7E−03 293 FXC1 7.8E−03 294 FLOT1 7.8E−03 295 CA12 7.8E−03 1 296HUS1 8.0E−03 297 EN2 8.0E−03 298 ITPR1 8.0E−03 299 HOXA1 8.2E−03 300WEE1 8.3E−03 301 CUL5 8.3E−03 302 LRRC16 8.3E−03 303 CAST 8.3E−03 304S100A10 8.4E−03 305 FXYD3 8.4E−03 306 UEVLD 8.4E−03 307 PRNP 8.5E−03 308TAPBPL 8.5E−03 309 PI3 8.5E−03 1 310 IL1A 8.6E−03 311 SUB1 8.6E−03 312PTRH2 8.6E−03 313 TXN 8.6E−03 314 MPL 8.7E−03 315 GSTO1 8.8E−03 316 KRAS8.8E−03 317 CNDP2 8.8E−03 318 IGFBP5 8.9E−03 319 MYCBP 8.9E−03 320 ANXA29.0E−03 321 TANK 9.1E−03 322 ZNF226 9.1E−03 323 CAPG 9.1E−03 324 TOB19.1E−03 325 C3orf28 9.2E−03 326 PKM2 9.2E−03 327 GAPDH 9.2E−03 328POLR2A 9.3E−03 329 SNUPN 9.4E−03 330 CHPF 9.4E−03 331 EIF5 9.4E−03 332CD151 9.4E−03 1 333 AK2 9.5E−03 334 LYPLA1 9.6E−03 335 CNR2 9.6E−03 336CRTAP 9.7E−03 337 ATF3 9.7E−03 338 RPL37A 9.8E−03 339 ICT1 9.8E−03 340PDCD10 9.8E−03 341 TNFRSF11B 9.8E−03 342 MOSC2 9.8E−03 343 CXCL3 9.8E−03344 TAF10 9.8E−03 345 IKBKE 9.9E−03 346 C12orf41 9.9E−03 347 FLJ102929.9E−03 348 PRR13 9.9E−03 349 SLFN12 1.0E−02 350 NPAS3 1.0E−02 351SCARB1 1.0E−02 352 ACACA 1.0E−02 353 SPCS1 1.0E−02 354 IPO7 1.0E−02 355CA3 1.0E−02 356 GGCX 1.0E−02 357 PSMA1 1.0E−02 358 ANXA5 1.1E−02 359SLC30A5 1.1E−02 360 ANGPT2 1.1E−02 1 361 AP4S1 1.1E−02 362 PLA2G2A1.1E−02 363 MPP6 1.1E−02 364 CCL8 1.1E−02 365 CTTN 1.1E−02 366 SERPINB61.1E−02 367 CDR2 1.1E−02 368 LEPR 1.1E−02 369 TMBIM4 1.1E−02 370 SSX2IP1.1E−02 371 RYR3 1.1E−02 1 372 TPST1 1.1E−02 373 SNRPA1 1.2E−02 374TMEM5 1.2E−02 375 ALG8 1.2E−02 376 TIMM8B 1.2E−02 377 PARVA 1.2E−02 378NDFIP1 1.2E−02 379 THOC7 1.2E−02 380 TBC1D15 1.2E−02 381 DNAJC6 1.2E−02382 EPPB9 1.2E−02 383 LSM4 1.2E−02 384 GLRA1 1.2E−02 385 UBB 1.2E−02 386MINA 1.2E−02 387 TRAPPC4 1.2E−02 388 SAR1B 1.3E−02 389 ANGEL2 1.3E−02390 TAF1B 1.3E−02 391 DIRAS3 1.3E−02 392 MLX 1.3E−02 393 HSPB7 1.3E−02394 C17orf75 1.3E−02 395 C5orf28 1.3E−02 396 CEBPB 1.3E−02 397 TRSPAP11.3E−02 398 RFK 1.3E−02 399 CNIH 1.3E−02 400 HSPA5 1.3E−02 401 GNS1.3E−02 402 CHPT1 1.3E−02 403 ELOVL6 1.3E−02 404 BNIP3 1.3E−02 405 COX5B1.3E−02 406 G6PC3 1.3E−02 407 ZNF143 1.4E−02 408 DUSP3 1.4E−02 409 YIPF21.4E−02 410 DOHH 1.4E−02 411 GNAT1 1.4E−02 412 ARF5 1.4E−02 413 PSPH1.4E−02 414 OSMR 1.4E−02 1 415 GALNT7 1.4E−02 416 HSPE1 1.4E−02 417SLC39A14 1.4E−02 418 FTL 1.4E−02 419 ANXA2P2 1.4E−02 420 SMC4 1.4E−02421 PDK1 1.4E−02 422 PSMC6 1.4E−02 423 TPD52L1 1.4E−02 424 PCDH8 1.4E−02425 ACTN1 1.5E−02 1 426 SWAP70 1.5E−02 427 FER1L4 1.5E−02 428 CHRNA21.5E−02 429 C17orf42 1.5E−02 430 MAS1 1.5E−02 431 IRF7 1.5E−02 432 PDCD61.5E−02 433 DHRS7B 1.5E−02 434 TMEM9B 1.5E−02 435 GLRX 1.5E−02 436 TMED71.5E−02 437 CCDC59 1.5E−02 438 CAPZA2 1.5E−02 439 ZNF552 1.5E−02 440BHLHB2 1.5E−02 1 441 FAM96B 1.5E−02 442 GPNMB 1.5E−02 443 SMPD1 1.5E−02444 TMCO3 1.5E−02 445 SNX3 1.5E−02 446 CHST2 1.5E−02 447 MGC3196 1.5E−02448 POLR2G 1.5E−02 449 LRP12 1.6E−02 450 CD47 1.6E−02 451 EXT2 1.6E−02452 CHMP2A 1.6E−02 453 EFEMP1 1.6E−02 454 TMEM14A 1.6E−02 455 IGF2BP31.6E−02 456 BCL3 1.6E−02 1 457 CHN2 1.6E−02 458 RARRES2 1.6E−02 459 FNTA1.7E−02 460 CPD 1.7E−02 461 CLEC5A 1.7E−02 462 LEF1 1.7E−02 463 SNX101.7E−02 464 PCDH9 1.7E−02 465 ABCC3 1.7E−02 466 ARHGAP29 1.7E−02 467ELOVL2 1.7E−02 468 NENF 1.7E−02 469 UNC50 1.7E−02 470 APITD1 1.7E−02 471ARPC4 1.7E−02 472 VIL2 1.7E−02 473 USP33 1.7E−02 474 POLR2C 1.7E−02 475PAM 1.7E−02 476 LZTFL1 1.7E−02 477 UTP6 1.7E−02 478 HIG2 1.7E−02 479MIA2 1.7E−02 480 STK3 1.8E−02 481 CPEB1 1.8E−02 482 GADD45B 1.8E−02 483RGS3 1.8E−02 484 C14orf109 1.8E−02 485 CFLAR 1.8E−02 486 SLC25A201.8E−02 487 VAMP5 1.8E−02 488 COMMD8 1.8E−02 489 ST8SIA5 1.8E−02 490SLC33A1 1.8E−02 491 IFRD1 1.8E−02 492 PLP2 1.8E−02 493 PLS3 1.8E−02 494PSMC3IP 1.8E−02 495 POSTN 1.8E−02 496 PCBD1 1.9E−02 497 CHI3L2 1.9E−02498 DUSP14 1.9E−02 499 LYRM2 1.9E−02 500 PPIC 1.9E−02 501 ATP5S 1.9E−02502 CFI 1.9E−02 503 GMPR 1.9E−02 504 ARMET 1.9E−02 505 HSP90B1 1.9E−02506 SLC4A3 1.9E−02 507 CASP3 1.9E−02 508 RHEB 1.9E−02 509 ATPBD1C1.9E−02 510 MAP7 1.9E−02 511 MGC5618 1.9E−02 512 ARPC5 1.9E−02 513 ACAA21.9E−02 514 FKBP1B 1.9E−02 515 Magmas 2.0E−02 516 UBE2NL 2.0E−02 517MTCH2 2.0E−02 518 AZGP1 2.0E−02 519 PPP1R15A 2.0E−02 520 BBS10 2.0E−02521 HOXA5 2.0E−02 522 HS2ST1 2.0E−02 523 ATP6V1D 2.0E−02 524 C11orf582.0E−02 525 STOML1 2.0E−02 526 HRH1 2.0E−02 1 527 TGFBI 2.0E−02 528ATP5G1 2.0E−02 529 CASP4 2.1E−02 530 TIAM2 2.1E−02 531 RGS16 2.1E−02 532SNAPC5 2.1E−02 533 GLS 2.1E−02 534 PUS1 2.1E−02 535 CHMP2B 2.1E−02 536C9orf53 2.1E−02 537 RRAS 2.1E−02 1 538 CHCHD7 2.1E−02 539 AKAP12 2.1E−02540 LARP6 2.1E−02 541 PPP3CC 2.1E−02 542 ATP5F1 2.2E−02 543 CLDN102.2E−02 544 ALAS1 2.2E−02 545 CHN1 2.2E−02 546 SACM1L 2.2E−02 547 IFI442.2E−02 548 PSMD14 2.2E−02 549 IL6 2.2E−02 550 FABP7 2.2E−02 551 ZNF5932.2E−02 552 RS1 2.2E−02 553 EFHC2 2.2E−02 554 B3GALNT1 2.3E−02 555 GRPR2.3E−02 556 EI24 2.3E−02 557 GINS4 2.3E−02 558 DLG1 2.3E−02 559 LTBP12.3E−02 560 LOX 2.3E−02 561 GLIPR1 2.3E−02 562 P4HA2 2.3E−02 563 RIMBP22.4E−02 564 MRPL2 2.4E−02 565 PLA2G5 2.4E−02 1 566 IER3IP1 2.4E−02 567MCFD2 2.4E−02 568 SRPX2 2.4E−02 569 EBNA1BP2 2.4E−02 570 RPL39L 2.4E−02571 TMED9 2.4E−02 572 RNASE1 2.4E−02 573 C14orf2 2.4E−02 574 BHLHB92.4E−02 575 ARL1 2.4E−02 576 TSC22D2 2.4E−02 577 EFNB2 2.4E−02 1 578PTPN21 2.4E−02 579 YAP1 2.5E−02 580 WSB2 2.5E−02 581 IL1RN 2.5E−02 582CYBRD1 2.5E−02 583 GUK1 2.5E−02 584 ORC5L 2.5E−02 585 XPOT 2.5E−02 586LIAS 2.5E−02 587 ITGB1BP1 2.5E−02 588 CTBS 2.5E−02 589 GTF2H1 2.5E−02590 TMEM106C 2.5E−02 591 COX17 2.5E−02 592 HOMER3 2.5E−02 1 593 SDC42.5E−02 594 DUSP5 2.5E−02 595 GPX3 2.6E−02 596 APIP 2.6E−02 597 IFRD22.6E−02 598 RPA3 2.6E−02 599 GGH 2.6E−02 600 HOXC10 2.6E−02 601 CD992.6E−02 602 HPCAL1 2.6E−02 603 FAH 2.6E−02 604 PPFIA4 2.6E−02 605C14orf45 2.6E−02 606 MC3R 2.6E−02 607 PIGG 2.6E−02 608 PCSK5 2.6E−02 609ITGA5 2.6E−02 1 610 RBKS 2.7E−02 611 C18orf10 2.7E−02 612 AUH 2.7E−02613 CD97 2.7E−02 1 614 RNF7 2.7E−02 615 PIGN 2.7E−02 616 C12orf242.7E−02 617 C11orf51 2.7E−02 618 DRAM 2.7E−02 619 CYP51A1 2.7E−02 620ANXA1 2.7E−02 621 PLAUR 2.7E−02 1 622 SHQ1 2.7E−02 623 CD46 2.8E−02 624RECQL 2.8E−02 625 KMO 2.8E−02 626 GUCA1A 2.8E−02 627 PDK3 2.8E−02 628PSMD9 2.8E−02 629 SPINK1 2.8E−02 630 UBE1C 2.8E−02 631 MTERFD1 2.8E−02632 RAGE 2.8E−02 633 PVR 2.8E−02 634 SLC35E3 2.8E−02 635 MMP12 2.9E−02636 NRGN 2.9E−02 637 CSDA 2.9E−02 638 ATP6V1C1 2.9E−02 639 PIK3C2A2.9E−02 640 PSMB3 2.9E−02 641 FGA 2.9E−02 642 PCGF1 2.9E−02 643 MRPL222.9E−02 644 SLC22A5 2.9E−02 645 HMOX1 2.9E−02 646 AQP1 2.9E−02 647 HR442.9E−02 648 CGRRF1 2.9E−02 649 PSMC2 2.9E−02 650 RMND5B 2.9E−02 651 CRP2.9E−02 652 MRPL23 2.9E−02 653 PEX16 3.0E−02 654 GABRB2 3.0E−02 655 GBAS3.0E−02 656 DLC1 3.0E−02 1 657 PPP2R1B 3.0E−02 658 CAMK1 3.0E−02 659SLC25A32 3.0E−02 660 SEPX1 3.0E−02 661 CDK10 3.0E−02 662 ADAM8 3.0E−02663 MSN 3.0E−02 664 PIR 3.0E−02 665 PMM2 3.1E−02 666 PLA2G3 3.1E−02 667MT1X 3.1E−02 668 NEDD4L 3.1E−02 669 ARPC2 3.1E−02 670 CD300A 3.1E−02 671ZCCHC10 3.1E−02 672 SLC3A1 3.1E−02 673 ABCA1 3.1E−02 674 ITGB5 3.1E−02675 ASS1 3.1E−02 676 BCAT1 3.1E−02 677 POT1 3.1E−02 678 UBE2N 3.2E−02679 DARS 3.2E−02 680 RINT1 3.2E−02 681 HSPB2 3.2E−02 682 NME5 3.2E−02683 KIAA0101 3.2E−02 684 VAV3 3.2E−02 685 TMEM111 3.2E−02 686 MAX3.2E−02 687 PSMB2 3.2E−02 688 TAAR5 3.3E−02 689 PDHX 3.3E−02 690 ZNF4153.3E−02 691 SEC24A 3.3E−02 692 CXCL5 3.3E−02 693 AMDHD2 3.3E−02 694SPATA6 3.3E−02 695 C9orf3 3.3E−02 696 C1QBP 3.3E−02 697 SEC24D 3.3E−02698 PSRC1 3.3E−02 699 LAMP2 3.3E−02 700 FKBP11 3.3E−02 701 LAMC1 3.3E−02702 CASP1 3.3E−02 703 MCL1 3.3E−02 704 SLC35A2 3.3E−02 705 C2orf283.3E−02 706 HCCS 3.4E−02 707 WDR61 3.4E−02 708 S100A14 3.4E−02 709 BDH13.4E−02 710 UFM1 3.5E−02 711 DKFZP586H2123 3.5E−02 712 CYP27A1 3.5E−02713 NIT2 3.5E−02 714 CSGlcA-T 3.5E−02 715 CD83 3.5E−02 716 GIP 3.5E−02717 DERL2 3.5E−02 718 MASP2 3.5E−02 719 PEX3 3.5E−02 720 NUPL1 3.5E−02721 GSDMDC1 3.5E−02 722 PCK2 3.5E−02 723 TFAP2C 3.6E−02 724 CLDN153.6E−02 725 KIAA1660 3.6E−02 726 PRMT3 3.6E−02 727 ECAT8 3.6E−02 728MS4A2 3.6E−02 729 IFI35 3.6E−02 730 SLC31A1 3.6E−02 731 ASNS 3.6E−02 732NRL 3.6E−02 733 PON2 3.6E−02 734 MPI 3.6E−02 735 OAS1 3.6E−02 736 BAG23.6E−02 737 NUPR1 3.6E−02 738 SLC35A5 3.6E−02 739 NUDT15 3.6E−02 740SDF2L1 3.6E−02 741 MDH2 3.6E−02 742 RER1 3.7E−02 743 SQRDL 3.7E−02 744SDS 3.7E−02 745 SNX2 3.7E−02 746 FLJ20035 3.7E−02 747 NAGLU 3.7E−02 748TTC27 3.7E−02 749 TRIP6 3.7E−02 750 COPS8 3.7E−02 751 C21orf62 3.7E−02752 FGF5 3.7E−02 753 TMEM168 3.7E−02 754 LEP 3.7E−02 755 KIAA06923.7E−02 756 MIS12 3.7E−02 757 CCR4 3.7E−02 758 CCNB1 3.7E−02 759C12orf47 3.8E−02 760 EMP1 3.8E−02 1 761 APOBEC3F 3.8E−02 762 GLB13.8E−02 763 CGA 3.8E−02 764 SRPRB 3.8E−02 765 KIAA0143 3.8E−02 766 NEK113.8E−02 767 REEP5 3.8E−02 768 NMI 3.8E−02 769 CXCL14 3.8E−02 770 TUFT13.8E−02 771 ADAM7 3.8E−02 772 NUBP2 3.8E−02 773 NEDD9 3.8E−02 774 LMO43.9E−02 775 CTSB 3.9E−02 776 KIAA0415 3.9E−02 777 TNFRSF1A 3.9E−02 778PRDX4 3.9E−02 779 HOXD11 3.9E−02 780 SH3BGR 3.9E−02 781 CNGA3 3.9E−02782 PHEX 3.9E−02 783 CNIH4 4.0E−02 784 YKT6 4.0E−02 785 RWDD3 4.0E−02786 AGTR1 4.0E−02 787 NRAS 4.0E−02 788 SLC4A7 4.0E−02 789 CCDC53 4.0E−02790 ZAK 4.0E−02 791 DYNC1LI1 4.0E−02 792 AP2S1 4.1E−02 793 PIGL 4.1E−02794 C1RL 4.1E−02 1 795 SNAPC1 4.1E−02 796 HOXA2 4.1E−02 797 CNNM14.1E−02 798 RASAL1 4.1E−02 799 RGS12 4.1E−02 800 PAQR3 4.1E−02 801HCG2P7 4.2E−02 802 DIABLO 4.2E−02 803 CCT6A 4.2E−02 804 SERPINE1 4.2E−021 805 ETV5 4.2E−02 806 IDS 4.2E−02 807 GSTM5 4.2E−02 808 TIMM44 4.2E−02809 PTPRR 4.2E−02 810 MEA1 4.2E−02 811 C1orf107 4.2E−02 812 XKR8 4.2E−02813 PPL 4.2E−02 814 MTHFS 4.3E−02 815 PHLDB1 4.3E−02 816 PHLDA2 4.3E−02817 SDF2 4.3E−02 818 LYRM1 4.3E−02 819 APOBEC3B 4.3E−02 820 CASP74.3E−02 821 TM9SF1 4.3E−02 822 TAX1BP3 4.4E−02 823 LACTB2 4.4E−02 824C9orf95 4.4E−02 825 TRIM36 4.4E−02 826 SIGLEC7 4.4E−02 827 SPRY1 4.4E−02828 POLR2H 4.4E−02 829 HTR5A 4.4E−02 830 WNT11 4.4E−02 831 IL6ST 4.4E−02832 COMMD9 4.4E−02 833 FAM82B 4.5E−02 834 MRPS18A 4.5E−02 835 FBXO94.5E−02 836 IBSP 4.5E−02 837 RPLP2 4.5E−02 838 NDUFB5 4.5E−02 839 RAB324.5E−02 840 PDLIM4 4.5E−02 1 841 OXTR 4.6E−02 842 MMP14 4.6E−02 1 843PSMB8 4.6E−02 844 LDLR 4.6E−02 845 DUSP4 4.6E−02 846 CCDC72 4.6E−02 847SS18L2 4.6E−02 848 PITX1 4.6E−02 849 LIF 4.6E−02 1 850 CRYBA2 4.6E−02851 LRRC50 4.6E−02 852 SNX11 4.7E−02 853 RFNG 4.7E−02 854 LAMP3 4.7E−02855 EBAG9 4.7E−02 856 ABCA5 4.7E−02 857 KIAA0323 4.8E−02 858 ACTR1B4.8E−02 859 CDKN3 4.8E−02 860 CD1A 4.8E−02 861 CSH1 4.8E−02 862 HOXC44.8E−02 863 SIPA1L1 4.8E−02 864 TMEM2 4.8E−02 865 CROT 4.8E−02 866PTDSS1 4.8E−02 867 HK3 4.8E−02 1 868 SRPR 4.8E−02 869 UCHL3 4.9E−02 870ANXA4 4.9E−02 871 YIPF4 4.9E−02 872 TRIAP1 4.9E−02 873 ZFYVE21 4.9E−02874 BST1 4.9E−02 875 SCN4A 4.9E−02 876 IFI6 4.9E−02 877 WTAP 4.9E−02 878MBD4 5.0E−02 879 HOXD10 5.0E−02 880 LOH11CR2A 5.0E−02 881 ZNF443 5.0E−02882 CTR9 5.0E−02 883 HOP 5.0E−02 884 CP 5.0E−02

TABLE 13 Table 13. MRs discovered by MRA and SLR using the TCGA data andTWPS signature. MGES Analysis TCGA Prognosis Analysis MRA- SLR- LR- MRA-LR- TF rank Overlap P-value rank Coeff rank Overlap P-value Coeff FOSL21 45 9.4E−39 5 0.21 4 69 1.9E−16 0.25 ZNF238 2 37 9.6E−28 2 −0.34 — — —— RUNX1 3 37 2.3E−24 4 0.13 — — — — C/EBP(*) 4 30 3.2E−19 1 0.40 1 911.0E−28 0.42 C/EBPδ 5 27 1.2E−19 6 0.42 3 75 1.8E−27 0.41 STAT3 6 261.2E−16 7 0.40 7 60 9.4E−17 0.21 BHLHB2 7 25 7.8E−21 9 0.41 2 78 5.3E−410.36 MYCN 8 25 6.2E−20 37 −0.11 — — — — FOSL1 9 23 3.6E−25 47 0.24 19 30 1.0E−11 0.28 ELF4 10 21 7.0E−09 34 0.1 — — — C/EBPβ 11 20 2.2E−15 280.35 10  45 1.5E−13 0.44 LZTS1 12 20 3.8E−14 3 0.22 — — — — TBX2 13 174.6E−12 23 0.17 21  28 1.6E−06 0.13 SATB1 14 17 1.4E−07 21 −0.32 — — — —IRF1 15 16 2.0E−11 19 0.48 — — — — EPAS1 16 16 2.6E−09 16 0.21 — — — —NFIB 17 15 5.4E−07 8 −0.32 — — — — KLF6 18 14 2.0E−11 — 0.16 — — — —NFYB 19 14 3.5E−07 14 −0.55 — — — — ELK3 20 14 1.8E−06 53 0.24 14  352.1E−05 0.19 ‘—’ indicate that TF is not significant in regulonenrichment analysis and not included in SLR analysis (*)The C/EBPmetagene includes targets of both C/EBPβ and C/EBPδ

TABLE 14 Immunohistochemistry results of GBM tumor specimens for C/EBPβand p-Stat3 and comparison with YKL-40 expression. STAT3− STAT3+ YKL40−12  2 YKL40+ 14 34 FET 0.00022 CEBPB− CEBPB+ YKL40− 9  5 YKL40+ 4 44 FET4.9E−05 DOUBLE− DOUBLE+ YKL40− 8  1 YKL40+ 2 32 FET 2.7E−06

Tumors were scored as positive or negative as described in the Methodsherein. Expression of either C/EBPβ or STAT3 was significantlyassociated with YKL40 expression (C/EBPβ, P=4.9×10⁻⁵; STAT3,P=2.2×10⁻⁴), with higher association in double-positive tumours (C/EBPβ⁺STAT3⁺, P=2.7×10⁻⁶) versus double-negative ones (C/EBPβ⁻ STAT3⁻, Table14).

TABLE 15 Primers used for ChIP assays. SEQ ID ChIP_Stat3 Primers NO:Rrpb1_3655_f1 ATCTGGATGGCATTTTCAGG 5 Rrpb1_3801_r1 GGGGTAACATTCGCAGTTGT6 Serpinh1_3546 CCTCACCATCTCTCCTTTGC 7 Serpinh1_3677_rGGGTCCCAAACACTTGAGAG 8 Chi3I1_3311_f CTGAGGTCTCTTGCCGAATC 9Chi3I1_3511_r TGTCGATGTGATCGTTGCTT 10 Timp1_2035_f GGTGGGTGGATGAGTAATGC11 Timp1_2194_r CCCTGCTTACCTCTGGTGTC 12 Socs3_f_1876_fGCGCTCAGCCTTTCTCTG 13 Socs3_r_2025_r GGAGCAGGGAGTCCAAGTC 14 Osmr_3468_fTGGGTGGGGTGTTTCATTAT 15 Osmr_3666_r GAACAAATGCTACGGGGAAA 16 Actn1_1054_fTAGATCACTCGGGGTTGTCC 17 Actn1_1290_r ACTGCTCTCAGAGGCTACCG 18Slc16a3_3601_ CCAGTGAGGTGCCAAATGT 19 Slc16a3_3731_r GACGCCCTGAGCTCTGTCT20 Col4a1_2620_f TTTGGGCGTATTTCTCCTTG 21 Col4a1_2806_rAGAAGGCAACGAGTTGAGGA 22 Col4a2_1023_f AGAAGGCAACGAGTTGAGGA 23Col4a2_1209_r TTTGGGCGTATTTCTCCTTG 24 Itga7_3019_f GCAGCAGCTGTAGCAGTGAG25 Itga7_3246_r GCCAAGGATACAGGCAACAT 26 Cd151_3821 AGGGGCATAGCCTGTCTGT27 Cd151_3983_r CAGGCCTGTTTACGGTCTGT 28 Icam1_365_f CCCAGGTGGATTTTTGTCTG29 Icam1_488_r ACAATGGTGCCGTTCTTTTC 30 Runx1_2719_f TGCGAGTAAGTTGTGCTGGT31 Runx1-2849_r CAGCATGCCGAGTTAAGGAT 32 Bhlhb2_1160_f TTCCCATGGGGTGACATC33 Bhlhb2_1277_r CAGAGGCTGGGGTTTCTTTC 34 Fosl2_275_fTGACCCCGAGTATTGTTTGG 35 Fosl2_423_r GGGGTGTTGGTAGCAGAGAA 36 Stat3_1501_fCAGGAGGGAGCTGTATCAGG 37 Stat3_1630_r AGGACTTGGGCACAGAAGC 38 ChIP_CEBPβPrimers Ptrf_1587_f GCAAGGGTCCTTTTGTGCT 39 Ptrf_1700_rGCTCATCCGAAAATCCTCAA 40 Shc1_1367_f2 CGCAACCACTTTGTTTTACG 41Shc1_1514_r2 GCTGAGGGCACAAGGAATTA 42 Mvp_3222_f2 CGGCTCCGTCCTTTGATAAC 43Mvp_3354_r2 AGCTCCCACTTCAGATGAGC 44 Serpine1_720_f GGGCTCCCACTGATTCTACA45 Serpine1_843_r ATGGTTTCGGGATGATTCAA 46 Timp1_2314_f2GGGCTAGTCTAGGGGGAAGA 47 Timp1_2390_r2 GGGGTTCTAGGGAGTTTGGA 48Serpina1_914_f4 TGTGCTGTCATCCAGAGTTTG 49 Serpina1_1057_r4GGGTCTAGTGCTGCTGATGA 50 S100a11_2551_f CATTGGCTCTCCACACCAG 51S100a11_2642_r ACATGTGTGTGCATGTGCTG 52 Slc26a3_2504_fCGCAACACCCTGAACACTC 53 Slc26a3_2580_r CACTTCCCTGCACGGTCT 54 Myl9_502_fTGGGATAACTGGCACAACCT 55 Myl9_571_r TCAGGACAATTTTCACATTGATT 56Stat3_2639_f2 CTGGCTGGTCGTGGGTAG 57 Stat3_2755_r2GGGAGCATAATTTAACCTAGAAAAAG 58 Cebpb_401_f ACCCCAGCTCAGCAGATAAC 59Cebpb_450_r ACCTCTCTGCCACTCCTAGC 60 Fosl2_516_f1 TCCTCATAAGGACCCTGTGG 61Fosl2_626_r1 TGTAGCGGAAGTCAGGGAAC 62 Runx1_1362_f AAGTTGTCCATTTAGGGGGAAT63 Bhlhb2_434_f1 TGGCCTCGATACAATTTTCC 69 Bhlhb2_555_r1TAGGCGCTGCACTAGTTGAT 65 ChIP_FosL2 Primers Actn1(2)_3581_fCAGCCAAAGGCATCCTGTAT 66 Actn1(2)_3711_r GGTCATCCTGCTTTGAGGAA 67Itga5(1)_1894_f GCGGGCTCAGAGTTCCAG 68 Itga5(1)2027_rCGCTTCCTAAACCTCCCAGA 69 Socs3_1734_f CCTTCGAACTTGCTTTGCAT 70Socs3_1816_r GCAGCCACCTAGACTTACCG 71 S100a11(1)_1185_fCTCCGGGACACCTGTGTATT 72 S100a11(1)_1308_r CTGAGGAGTGGATGCATGTG 73c1r(3)_3178_f ACTGAGGGGAGAAGCACAGA 74 c1r(3)_3308_r AAGCTGAGGCACAGTGGTTT75 Flna(2)_3196_f CACCCACCTCCTGACACTCT 76 Flna(2)_3295_rCTGGGTTGTCTGGGTTCATT 77 Tagln_1202_f TATTGACACTGCCCACTGGA 78Tagln_1351_r CACCCTTTCAATTGGACCAC 79 Emp3_2706_f TCCCTGGTGCTTAGAGATGG 80Emp3_2848_r CCGACATCAGGATTGAGGAG 81 Plau_278_f TTGGCTCTGAAGCCTATAGCA 82Plau_420_r CCTGCTGGGGAAAGTACAAG 83 Thbd(1)_38_f AACGAGGTTCCTGCCCTTAT 84Thbd(1)_180_r AGTCAAGCTGTGGCTGCCTA 85 Tnc(2)_1060_f ACTCCCTTAAATGCCCCTGT86 Tnc(2)_1180_r ATAAGCTGCGCCTTTGCTT 87 Acta2_135_f AAAATTCACAGGGCTGTTGC88 Acta2_580_r TCTCTGGCCCTGTAACTTGC 89 Ehd2_645_f2 AGGGGAGAGAGTGAGGCATT90 Ehd2_814_r2 CCACCTACATCTCCCCTGTC 91 Bace2(2)_1027_fCAGCTGGAGGAGGTACAAAGA 92 Bace2(2)_1175_r GCCAAGACGCAGAAATGC 93Slc16a3_282_f GGCAGATGTGGAAGGTGTCT 94 Slc16a3_415_r GGGTCCCCTATGGGGTATT95 Runx1_3473_f5 TGATGGTTTGGCAAAGCTG 96 Runx1_3609_r5GCATTCCCCTGCTCACTTAG 97 Stat3_3395_f3 TTGGTTCAGCCAGTTTTCTATC 98Stat3_3544_r3 TCCAGACTTGTTTCCCCATC 99 Cebpb_1129_f1 GATTGCAGCTGGGAGAAGTG100 Cebpb_1278_r1 CTGCTCGAGGCTTGGACAC 101 Fosl2_434_f1CCACCCCCAGTTTTCTGAG 102 Fosl2_551_r1 GGCTTGCCTGGGTGTTTAC 103Bhlhb2_1361_f2 GGGCTGGAGCTAGCAAGG 104 Bhlhb2_1503_r2AGGGGGAGAAGTTGGTAACG 105 ChIP_b-HLH-B2 Primers Serpine1_1227_fTCAGGGGCACAGAGAGAGTC 106 Serpine1_1375_r CAGCCACGTGATTGTCTAGG 107Efemp2_885_f ATGGTGGTGGCAGAGTGG 108 Efemp2_1027_r CTGCTTATCCCCGCAGTC 109Slc16a3_3151_f GGAGGGAAGGAACTGAGGAG 110 Slc16a3_3290_rACCCCAGACTCTGTCCACAC 111 Bcl3_1175_f AGCCCCTTTAGACCCACAG 112 Bcl3_1318_rAGCCGTTTCCTCCTTAGTGG 113 Pdpn_2946_f GCTTCCGAGGAGTGTGAGTG 114Pdpn_3046_r CACTGATGTTGTTGCCCAAG 115 Ifitm3_3298_f GAGCCGAGTCCTGTATCAGC116 Ifitm3_3443_r CCTGCTCAGTCTCAGAACCAC 117 Flna_3577_fGCACCCCCTAACACCACTAC 118 Flna_3718_r CATGCCCAAATATGGTTGAC 119Fcgr2c_112_f GCCAATTTACCGAGAGCAAG 120 Fcgr2c_214_r TGGAGGGGAAAGAGGAAGAG121 Socs3_1058_f ACCTCCCTGAACCTGAGTTG 122 Socs3_1207_rACAAGGCAGGCATTCTCATC 123 Slc39a8_523_f CCTGATGAAAGGCAAGAACG 124Slc39a8_1030_r GGACTTCCTGAGGCTGTGTC 125 Lif_82_f CCTGGTCACATGGATTTGG 126Lif_219_r ATCTCCTGCACAAGGACCTG 127 Runx1_674_f TTTCTGAAGTGCCTGTGCTG 128Runx1_815_r GCTCTGCTCTGCCTACATCC 129 Stat3_1376_f AGGAGTTGGGTCCCCAGAG130 Stat3_1520_r CCTGATACAGCTCCCTCCTG 131 Cebpb_3549_f2TTTCGAAGTTGATGCAATCG 132 Cebpb_3673_r AACAAGCCCGTAGGAACATC 133 Olr_fACTGCACCTGGCCAACTTTT 134 Olr_r TGCAAAGAAAAGAATACACAAAGGA 135

TABLE 16 Primers used for qRT-PCR. Primers SEQ mesenchymal genes IDmouse Sequence (5′-3′) NO: mSerpinh1_f GCCGAGGTGAAGAAACCCC 136mSerpinh1_r CATCGCCTGATATAGGCTGAAG 137 mCol4a1f CCAGGTGAAAGGGGAGAAAAAG138 mCol4a1_r CCAGGTTGACACTCCACAATG 139 mPlau_f CCTTCAGAAACCCTACAATGCC140 mPlau_r CAAACTGCCTTAGGCCAATCT 141 mActa2f GGACGTACAACTGGTATTGTGC 142mActa2_r CGGCAGTAGTCACGAAGGAAT 193 mSocs3_f TGCGCCTCAAGACCTTCAG 144mSocs3_r GAGCTGTCGCGGATCAGAAA 195 mSerpine1_f CATCCCCCATCCTACGTGG 146mSerpine1_r CCCCATAGGGTGAGAAAACCA 147 mItga7_qPCR_F1CTGCTGTGGAAGCTGGGATTC 148 mItga7_qPCR_R1 CTCCTCCTTGAACTGCTGTCG 149mOsmr_qPCR_f1 CATCCCGAAGCGAAGTCTTGG 150 mOsmr_qPCR_r1GGCTGGGACAGTCCATTCTAAA 151 mTimpl_qPCR_f1 CTTGGTTCCCTGGCGTACTC 152mTimpl_qPCR_r1 ACCTGATCCGTCCACAAACAG 153 mPlaur_qPCR_f1CAGAGCTTTCCACCGAATGG 154 mPlaur_qPCR_r1 GTCCCCGGCAGTTGATGAG 155 mGapdh_fTGACCACAGTCCATGCCATC 156 mGapdh_r GACGGACACATTGGGGGTAG 157 mCtgf_fGGGCCTCTTCTGCGATTTC 158 mCtgf_r ATCCAGGCAAGTGCATTGGTA 159 mFibfonectin_fGCAGTGACCACCATTCCTG 160 mFibronectin_r GGTAGCCAGTGAGCTGAACAC 161mCyr61_f CTGCGCTAAACAACTCAACGA 162 mCyr61_r GCAGATCCCTTTCAGAGCGG 163mSparc_f GTGGAAATGGGAGAATTTGAGGA 164 mSparc_r CTCACACACCTTGCCATGTTT 165mActn1_f GACCATTATGATTCCCAGCAGAC 166 mActn1_r CGGAAGTCCTCTTCGATGTTCTC167 mBace2_f GGAGCCTGTCAGGGCTACT 168 mBace2_r CCACAAGAATCTGTACCTTCTGC169 mGfap_f CGGAGACGCATCACCTCTG 170 mGfap_r AGGGAGTGGAGGAGTCATTCG 171mDoublecortin_f AAACTGGAAACCGGAGTTGTC 172 m_Doublecortin_rCGTCTTGGTCGTTACCTGAGT 173 m_Olig2_f CTGGTGTCTAGTCGCCCATC 174 m_Olig2_rGGGCTCAGTCATCTGCTTCT 175 mBetaIIITubulin_f TGGACAGTGTTCGGTCTGG 176mBetaIIITubulin_r CCTCCGTATAGTGCCCTTTGG 177 rCebpb_fATCGACTTCAGCCCCTACCT 178 rCebpb_r GGCTCACGTAACCGTAGTCG 179 m18s_fTCAAGAACGAAAGTCGGAGG 180 m18s_r GGACATCTAAGGGCATCACA 181 mStat3_fTGGCACCTTGGATTGAGAGTC 182 mStat3_r GCAGGAATCGGCTATATTGCT 183 mChi3I1_fGTACAAGCTGGTCTGCTACTTC 184 mChi3I1_r ATGTGCTAAGCATGTTGTCGC 185 mβActin_fGATGACGATATCGCTGCGCTG 186 mβActin_f GTACGACCAGAGGCATACAGG 187 Primersmesenchymal genes human Sequence (5′-3′) hSOCS3_f GAGCTGTCGCGGATCAGAAA188 hSOCS3_r TGACCAACATTGATAGCTCAGAC 189 hlTGA7_f GCGCAGGATAACCACAGCA190 hlTGA7_r AGGATTGAAACATCCAATGTCA 191 hOSMR_f GCTCCAGAAATTTGGCTCAG 192hOSMR_r CCACCCTAATCAAGGAAATGA 193 hCHl3L1_f TGAAATCCAGGTGTTGGGATA 194hCHl3L1_r TCAAGATGACCAAGATGTATAAAGG 195 hTIMP1_f GCAGTTTTCCAGCAATGAGA196 hTIMP1_r CTGACATTCCCAAGGAGGAG 197 hSTAT3_136_f AGGTGAGGGACTCAAACTGC198 hSTAT3_331_r ATCGACTTCAGCCCGTACC 199 hCEBP13_412 _fCCGTAGTCGTCGGAGAAGAG 200 hCEBP13_575_r CGCCGCTAGAGGTGAAATTC 201 h18s-fCGCCGCTAGAGGTGAAATTC 202 h18s-r CTTTCGCTCTGGTCCGTCTT 203 hCOL1A2_fTCTGGATGGATTGAAGGGACA 204 hCOL1A2_r CCAACACGTCCTCTCTCACC 205 hFN_fGAAGGCTTGAACCAACCTACG 206 hFN_r TGATTCAGACATTCGTTCCCAC 207 hCDH11_fTCCCAGGGAAGACATGAGATT 208 hCDH11_r TGTAGCCACCACATAGAGGAA 209 hTNC_fGCACACAGTAGATGGGGAAAA 210 hTNC_r CAGCAGCTCCTTAACATCAGG 211 hIGFBP5_fTGTGACCGCAAAGGATTCTAC 212 hIGFBP5_r GCAGCTTCATCCCGTACTTG 213 hCOL5A1_fGCATTTCCCGAGGACTTCTCC 214 hCOL5A1_r AATCTGCTGGATACCCTGCTC 215 hFOSL2_fTATCCCGGGAACTTTGACAC 216 hFOSL2_r TGAGCCAGGCATATCTACCC 217 hBHLHB2_fCAGCAGCAGAAAATCATTGC 218 hBHLHB2_r TTCAGGTCCCGAGTGTTCTC 219 hRUNX1_fCCCATCGCTTTCAAGGTG 220 hRUNX1_r TGGTCAGAGTGAAGCTTTTCC 221 hCTGF_fGGCAAAAAGTGCATCCGTACT 222 hCTGF_r CCGTCGGTACATACTCCACAG 223

TABLE 17 shRNA sequences Gene Gene ID TRC number Clone ID Stat3 6774TRCN0000020843 NM_003150.2-361s1c1 SEQ IDCCGGGCAAAGAATCACATGCCACTTCTCGAGAAGTGGCATGTGATTCTTTGCTTTTT NO: 224 C/EBPβ1051 TRCN0000007442 NM_005194.2-540s1c1 SEQ IDCCGGCGACTTCCTCTCCGACCTCTTCTCGAGAAGAGGTCGGAGAGGAAGTCGTTTTT NO: 225 Fosl22355 TRCN0000016142 NM_005253.3-1368s1c1 SEQ IDCCGGCACGGCCCAGTGTGCAAGATTCTCGAGAATCTTGCACACTGGGCCGTGTTTTT NO: 226 bHLHB28553 TRCN0000013249 NM_003670.1-512s1c1 SEQ IDCCGGGCACTAACAAACCTAATTGATCTCGAGATCAATTAGGTTTGTTAGTGCTTTTT NO: 227 Runx1 861 TRCN0000013660 NM_001754.2-1051s1c1 SEQ IDCCGGCCTCGAAGACATCGGCAGAAACTCGAGTTTCTGCCGATGTCTTCGAGGTTTTT NO: 228

-   Aoki, K., Meng, G., Suzuki, K., Takashi, T., Kameoka, Y., Nakahara,    K., lshida, R., and Kasai, M. (1998). RP58 associates with condensed    chromatin and mediates a sequence-specific transcriptional    repression. J Biol Chem 273, 26698-26704.-   Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H.,    Chemy, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J.    T., et al. (2000). Gene ontology: tool for the unification of    biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29.-   Bachoo, R. M., Maher, E. A., Ligon, K. L., Sharpless, N. E.,    Chan, S. S., You, M. J., Tang, Y., DeFrances, J., Stover, E.,    Weissleder, R., et al. (2002). Epidermal growth factor receptor and    Ink4a/Arf: convergent mechanisms governing terminal differentiation    and transformation along the neural stem cell to astrocyte axis.    Cancer Cell 1,269-277.-   Barnabe-Heider, F., Wasylnka, J. A., Fernandes, K. J., Porsche, C.,    Sendtner, M., Kaplan, D. R., and Miller, F. D. (2005). Evidence that    embryonic neurons regulate the onset of cortical gliogenesis via    cardiotrophin-1. Neuron 48, 253-265.-   Basso, K., Margolin, A. A., Stolovitzky, G., Klein, U.,    Dalla-Favera, R., and Califano, A. (2005). Reverse engineering of    regulatory networks in human B cells. Nat Genet. 37, 382-390.-   Bonni, A., Sun, Y., Nadal-Vicens, M., Bhatt, A., Frank, D. A.,    Rozovsky, 1., Stahl, N., Yancopoulos, G. D., and Greenberg, M. E.    (1997). Regulation of gliogenesis in the central nervous system by    the JAK-STAT signaling pathway. Science 278, 477-483.-   Bromberg, J. F., Wrzeszczynska, M. H., Devgan, G., Zhao, Y.,    Pestell, R. G., Albanese, C., and Darnell, J. E., Jr. (1999). Stat3    as an oncogene. Cell 98, 295-303.-   Bussemaker, H. J., L1, H., and Siggia, E. D. (2001). Regulatory    element detection using correlation with expression. Nat Genet. 27,    167-171.-   Demuth, T., and Berens, M. E. (2004). Molecular mechanisms of glioma    cell migration and invasion. J Neurooncol 70, 217-228.-   Frank, S. R., Schroeder, M., Fernandez, P., Taubert, S., and    Amati, B. (2001). Binding of c-Myc to chromatin mediates    mitogen-induced acetylation of histone H4 and gene activation. Genes    Dev 15, 2069-2082.-   Freije, W. A., Castro-Vargas, F. E., Fang, Z., Horvath, S.,    Cloughesy, T., Liau, L. M., Mischel, P. S., and Nelson, S. F.    (2004). Gene expression profiling of gliomas strongly predicts    survival. Cancer Res 64, 6503-6510.-   Fuks, F., Burgers, W. A., Godin, N., Kasai, M., and Kouzarides, T.    (2001). Dnmt3a binds deacetylases and is recruited by a    sequence-specific repressor to silence transcription. Embo J 20,    2536-2544.-   Hanauer, D. A., Rhodes, D. R., Sinha-Kumar, C., and    Chinnaiyan, A. M. (2007). Bioinformatics approaches in the study of    cancer. Curr Mol Med 7, 133-141.-   He, F., Ge, W., Martinowich, K., Becker-Catania, S., Coskun, V.,    Zhu, W., Wu, H., Castro, D., Guillemot, F., Fan, G., et al. (2005).    A positive autoregulatory loop of Jak-STAT signaling controls the    onset of astrogliogenesis. Nat Neurosci 8, 616-625.-   Hoelzinger, D. B., Demuth, T., and Berens, M. E. (2007). Autocrine    factors that sustain glioma invasion and paracrine biology in the    brain microenvironment. J Natl Cancer Inst 99, 1583-1593.-   Kalir, S., Mangan, S., and Alon, U. (2005). A coherent feed-forward    loop with a SUM input function prolongs flagella expression in    Escherichia coli. Mol Syst Biol 1, 2005 0006.-   Kargiotis, 0., Rao, J. S., and Kyritsis, A. P. (2006). Mechanisms of    angiogenesis in gliomas. J Neurooncol 78, 281-293.-   Lander, A. D. (2004). A calculus of purpose. PLoS Biol 2, e164.-   Lee, J., Kotliarova, S., Kotliarov, Y., L1, A., Su, Q., Donin, N.    M., Pastorino, S., Purow, B. W., Christopher, N., Zhang, W., et al.    (2006). Tumor stem cells derived from glioblastomas cultured in bFGF    and EGF more closely mirror the phenotype and genotype of primary    tumors than do serum-cultured cell lines. Cancer Cell 9, 391-403.-   Lee, J. P., Jeyakumar, M., Gonzalez, R., Takahashi, H., Lee, P. J.,    Baek, R. C., Clark, D., Rose, H., Fu, G., Clarke, J., et al (2007).    Stem cells act through multiple mechanisms to benefit mice with    neurodegenerative metabolic disease. Nat Med 13, 439-447.-   Mani, K. M., Lefebvre, C., Wang, K., Lim, W. K., Basso, K., Dalla    Favera, R., and Califano, A. (2007). A Systems biology approach to    prediction of oncogenes and perturbation targets in B cell    lymphomas. Molecular Systems Biology in press.-   Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky,    G., Dalla Favera, R., and Califano, A. (2006a). ARACNE: an algorithm    for the reconstruction of gene regulatory networks in a mammalian    cellular context. BMC Bioinformatics 7 Suppl 1, S7.-   Margolin, A. A., Wang, K., Lim, W. K., Kustagi, M., Nemenman, I.,    and Califano, A. (2006b). Reverse engineering cellular networks. Nat    Protoc 1, 662-671.-   Menard, C., Hein, P., Paquin, A., Savelson, A., Yang, X. M.,    Lederfein, D., Barnabe-Heider, F., Mir, A. A., Stemeck, E.,    Peterson, A. C., et al. (2002). An essential role for a MEK-C/EBP    pathway during growth factor-regulated cortical neurogenesis. Neuron    36, 597-610.-   Nadeau, S., Hein, P., Fernandes, K. J., Peterson, A. C., and    Miller, F. D. (2005). A transcriptional role for C/EBP beta in the    neuronal response to axonal injury. Mol Cell Neurosci 29, 525-535.-   Nakashima, K., Yanagisawa, M., Arakawa, H., Kimura, N., Hisatsune,    T., Kawabata, M., Miyazono, K., and Taga, T. (1999). Synergistic    signaling in fetal brain by STAT3-Smadl complex bridged by p300.    Science 284, 479-482.-   Network, A. (2008). Comprehensive genomic characterization defines    human glioblastoma genes and core pathways. Nature 455, 1061-1068.-   Niehof, M., Kubicka, S., Zender, L., Manns, M. P., and Trautwein, C.    (2001). Autoregulation enables different pathways to control    CCAAT/enhancer binding protein beta (C/EBP beta) transcription. J    Mol Biol 309, 855-868.-   Nigro, J. M., Misra, A., Zhang, L., Smirnov, I., Colman, H.,    Griffin, C., Ozburn, N., Chen, M., Pan, E., Koul, D., et al. (2005).    Integrated array-comparative genomic hybridization and expression    array profiles identify clinically relevant molecular subtypes of    glioblastoma. Cancer Res 65, 1678-1686.-   Ohgaki, H., and Kleihues, P. (2005). Population-based studies on    incidence, survival rates, and genetic alterations in astrocytic and    oligodendroglial gliomas. J Neuropathol Exp Neurol 64, 479-489.-   Palomero, T., Lim, W. K., Odom, D. T., Sulis, M. L., Real, P. J.,    Margolin, A., Barnes, K. C., O'Neil, J., Neuberg, D., Weng, A. P.,    et al. (2006). NOTCH I directly regulates c-MYC and activates a    feed-forward-loop transcriptional network promoting leukemic cell    growth. Proc Natl Acad Sci USA 103, 18261-18266.-   Paquin, A., Barnabe-Heider, F., Kageyama, R., and Miller, F. D.    (2005). CCAAT/enhancer-binding protein phosphorylation biases    cortical precursors to generate neurons rather than astrocytes in    vivo. J Neurosci 25, 10747-10758.-   Park, K. I., Hack, M. A., Ourednik, J., Yandava, B., Flax, J. D.,    Stieg, P. E., Gullans, S., Jensen, F. E., Sidman, R. L., Ourednik,    V., et al. (2006). Acute injury directs the migration,    proliferation, and differentiation of solid organ stem cells:    evidence from the effect of hypoxia-ischemia in the CNS on clonal    “reporter” neural stem cells. Exp Neurol 199, 156-178.-   Parker, M. A., Anderson, J. K., Corliss, D. A., Abraria, V. E.,    Sidman, R. L., Park, K. I., Teng, Y. D., Cotanche, D. A., and    Snyder, E. Y. (2005). Expression profile of an operationally-defined    neural stem cell clone. Exp Neurol 194, 320-332.-   Pelloski, C. E., Mahajan, A., Maor, M., Chang, E. L., Woo, S.,    Gilbert, M., Colman, H., Yang, H., Ledoux, A., Blair, H., et al.    (2005). YKL-40 expression is associated with poorer response to    radiation and shorter overall survival in glioblastoma. Clin Cancer    Res 11, 3326-3334.-   Phillips, H. S., Kharbanda, S., Chen, R., Forrest, W. F.,    Soriano, R. H., Wu, T. D., Misra, A., Nigro, J. M., Colman, H.,    Soroceanu, L., et al. (2006). Molecular subclasses of high-grade    glioma predict prognosis, delineate a pattern of disease    progression, and resemble stages in neurogenesis. Cancer Cell 9,    157-173.-   Ramji, D. P., and Foka, P. (2002). CCAAT/enhancer-binding proteins:    structure, function and regulation. Biochem J 365, 561-575.-   Rhodes, D. R., and Chinnaiyan, A. M. (2005). Integrative analysis of    the cancer transcriptome. Nat Genet. 37 Suppl, S31-37.-   Rothschild, G., Zhao, X., Iavarone, A., and Lasorella, A. (2006). E    Proteins and ld2 Converge on p57Kip2 To Regulate Cell Cycle in    Neural Cells. Mol Cell Biol 26, 4351-4361.-   Simmons, M. L., Lamborn, K. R., Takahashi, M., Chen, P., Israel, M.    A., Berger, M. S., Godfrey, T., Nigro, J., Prados, M., Chang, S., et    al. (2001). Analysis of complex relationships between age, p53,    epidermal growth factor receptor, and survival in glioblastoma    patients. Cancer Res 61, 1122-1128.-   Sterneck, E., and Johnson, P. F. (1998). CCAAT/enhancer binding    protein beta is a neuronal transcriptional regulator activated by    nerve growth factor receptor signaling. J Neurochem 70, 2424-2433.-   Takashima, Y., Era, T., Nakao, K., Kondo, S., Kasuga, M., Smith, A.    G., and Nishikawa, S. (2007). Neuroepithelial cells supply an    initial transient wave of MSC differentiation. Cell 129, 1377-1388.-   Tarin, D., Thompson, E. W., and Newgreen, D. F. (2005). The fallacy    of epithelial mesenchymal transition in neoplasia. Cancer Res 65,    5996-6000; discussion 6000-5991.-   Tegner, J., Yeung, M. K., Hasty, J., and Collins, J. J. (2003).    Reverse engineering gene networks: integrating genetic perturbations    with dynamical modeling. Proc Natl Acad Sci USA 100, 5944-5949.-   Tso, C. L., Shintaku, P., Chen, J., Liu, Q., Liu, J., Chen, Z.,    Yoshimoto, K., Mischel, P. S., Cloughesy, T. F., Liau, L. M., et al.    (2006). Primary glioblastomas express mesenchymal stem-like    properties. Mol Cancer Res 4, 607-619.-   Visted, T., Enger, P. O., Lund-Johansen, M., and Bjerkvig, R.    (2003). Mechanisms of tumor cell invasion and angiogenesis in the    central nervous system. Front Biosci 8, e289-304.-   Wurmser, A. E., Nakashima, K., Summers, R. G., Toni, N., D'Amour, K.    A., Lie, D. C., and Gage, F. H. (2004). Cell fusion-independent    differentiation of neural stem cells to the endothelial lineage.    Nature 430, 350-356.-   Zhao, X., D, D. A., Lim, W. K., Brahmachary, M., Carro, M. S.,    Ludwig, T., Cardo, C. C., Guillemot, F., Aldape, K., Califano, A.,    et al. (2009). The N-Myc-DLL3 Cascade Is Suppressed by the Ubiquitin    Ligase Huwel to Inhibit Proliferation and Promote Neurogenesis in    the Developing Brain. Dev Cell 17, 210-221.-   Zhao, X., Heng, J. I., Guardavaccaro, D., Jiang, R., Pagano, M.,    Guillemot, F., Iavarone, A., and Lasorella, A. (2008). The    HECT-domain ubiquitin ligase Huwel controls neural differentiation    and proliferation by destabilizing the N-Myc oncoprotein. Nat Cell    Biol 10, 643-653.

Example 9 Transient Analysis of Reporters Transfected into Glioma Cells

SNB19 human glioma cells were transiently transfected with the plasmidsexpressing luciferase under the control of the indicated Stat3 orC/EBPbeta binding sites in the presence or absence of siRNAoligonucleotides targeting Stat3 or C/EBPbeta, respectively (FIG. 35).Luciferase activity was measured on a luminometer and the results areshown after normalization with a control renilla-expression vectordriven by a CMV-promoter plasmid. Stat3-driven luciferase activity isefficiently down-regulated in cells with silenced Stat3 expression andC/EBPbeta-driven luciferase activity is partially reduced in cells withsilenced C/EBPbeta expression.

SNB19 human glioma cells were stably transfected with theC/EBPbeta-driven luciferase plasmid (FIG. 36). Several clones wereisolated and propagated. Results are shown for clone #9 in combinationwith cells expressing a control renilla-expression vector driven by aCMV-promoter plasmid (clone #19) (FIG. 36). Cells were transfected withcontrol siRNAs or siRNA oligonucleotides targeting C/EBPbeta (forexample, SEQ ID NO: 228 or SEQ ID NO: 229). The control siRNA sequenceis the Dharmacon ON-TARGETpIus Non-targeting Pool (Cat#:D-001810-10-20). Luciferase activity was measured on a luminometer andthe results are shown after normalization with renilla. C/EBPbeta-drivenluciferase activity is efficiently down-regulated in cells with silencedC/EBPbeta expression.

SNB19 human glioma cells were stably transfected with theC/EBPbeta-driven luciferase plasmid (FIG. 37). Several clones wereisolated and propagated. Results are shown for clone #9 in combinationwith cells expressing a control renilla-expression vector driven by aCMV-promoter plasmid (clone #19). Cells were transfected with controlsiRNAs or two different siRNA oligonucleotides targeting C/EBPbeta(siCEBPb05: CCUCGCAGGUCAAGAGCAA [SEQ ID NO: 228]; and siCEBP06:CUGCUUGGCUGCUGCGUAC [SEQ ID NO: 229]) (FIG. 37). The control siRNAsequence is the Dharmacon ON-TARGETplus Non-targeting Pool (Cat#:D-001810-10-20). Luciferase activity was measured on a luminometer andthe results are shown after normalization with renilla. There is acorrelation between the efficiency of down-regulation ofC/EBPbeta-driven luciferase activity and the efficiency of silencingC/EBPbeta expression.

SNB19 human glioma cells will be stably transfected with theStat3-driven luciferase plasmid (FIG. 35). Several clones will beisolated and propagated. Cells will then be transfected with controlsiRNAs or an siRNA oligonucleotide targeting Stat3 (for example,CAGCCUCUCUGCAGAAUUCAA [SEQ ID NO: 230). The control siRNA sequence usedwill be the Dharmacon ON-TARGETpIus Non-targeting Pool (Cat#:D-001810-10-20). Luciferase activity will be measured on a luminometerand the results will be normalized with renilla.

SNB19 human glioma cells will also be stably transfected with either aC/EBPδ-driven luciferase plasmid, a RunX1-driven luciferase plasmid, aFosL2-driven luciferase plasmid, a bHLH-B2-driven luciferase plasmid, ora ZNF238-driven luciferase plasmid. Several clones will be isolated andpropagated. Cells expressing a C/EBPδ-driven luciferase plasmid, aRunX1-driven luciferase plasmid, a FosL2-driven luciferase plasmid, abHLH-B2-driven luciferase plasmid, or a ZNF238-driven luciferase plasmidwill then be transfected with control siRNAs or an siRNAoligonucleotide(s) targeting either C/EBPδ, RunX1, FosL2, bHLH-B2, orZNF238, respectively. The control siRNA sequence used will be theDharmacon ON-TARGETpIus Non-targeting Pool (Cat#: D-001810-10-20).Luciferase activity will be measured on a luminometer and the resultswill be normalized with renilla.

Example 10 Identification of Compounds that Interfere withC/EBP-Mediated Transcriptional Activity

A screening for the identification of compounds that could specificallyinterfere with C/EBP-mediated transcriptional activity was developed inthe mesenchymal glioma cell line SNB19. A multimerizedC/EBPbeta-luciferase reporter was stably introduced in SNB19 and ascreening of ˜9,800 compounds was done to identify positive candidates.In a first pilot screen with 2000 compounds, the chemotherapeutic drugetoposide was the most specific and potent compound identified byscreening a microsource library (2000 compounds) and was found to notonly inhibit the CCAAT/enhancer-binding protein (CEBP) luciferasereporter, but also the active form of the signal transducer andactivator of transcription 3 protein (phospho-STAT3). From the laterscreen of ˜9,800 compounds, molecules were identified for their abilityto inhibit the reporter signal >50%. The list of the molecules isincluded in Table 18. Further studies are aimed to determine specificityand validate in multiple in vitro and in vivo systems of glioma.

Additional compounds have also been tested for inhibition of C/EBPbactivity including 5-fluorouracil and Toxin B from clostridiumdifticilis. Graphs showing inhibition using a C/EBPb gene reporter assayfor both compounds are shown in FIG. 38 and FIG. 39. FIG. 38A showsCEBPb reporter activity at 48 hr upon inhibition with various dosages of5-fluorouracil (5-FU). FIG. 38B shows ATP cell viability at 24 hr and 48hr upon inhibition with various dosages of 5-FU. FIG. 39A shows CEBPbreporter activity at 48 hr upon inhibition with various dosages ofclostridium difficilis Toxin B (CD Toxin B). FIG. 39B shows ATP cellviability at 24 hr and 48 hr upon inhibition with various dosages of CDToxin B.

TABLE 18 Compounds that inhibit C/EBPbeta-luciferase reporter signal >50%. ID Vendor Structure STOCK6S-71833 IBS

BAS 00293383 Asinex

BAS 00702176 Asinex

ST057175 TimTech

F3205-0060 Life

9123281 ChemBridge

BAS 01109087 Asinex

T5756459 Enamine

T0505-3249 Enamine

BAS 00389694 Asinex

STOCK6S-69648 IBS

STOCK1S-41380 IBS

F1065-0197 Life

ASN 16287147 Asinex

BAS 00873812 Asinex

T6261669 Enamine

5338884 ChemBridge

STOCK2S-10951 IBS

STOCK6S-65265 IBS

STOCK1S-62600 IBS

BAS 02946522 Asinex

BAS 00318863 Asinex

T5225535 Enamine BAS 02256215 Asinex

T5756959 Enamine

5106399 ChemBridge

ST057180 TimTech

STOCK2S-14814 IBS

T0519-1108 Enamine

T5552290 Enamine

ST084242 TimTech

STOCK4S-73514 IBS

BAS 02140954 Asinex

STOCK6S-76426 IBS

BAS 02592298 Asinex

5570087 ChemBridge

T5636062 Enamine

ASN 07731410 Asinex

T5414273 Enamine

BAS 01236576 Asinex

T5691624 Enamine

STOCK6S-83108 IBS

ST079841 TimTech

STOCK3S-62210 IBS

6102842 ChemBridge

STOCK2S-03855 IBS

ASN 19852408 Asinex

T5644376 Enamine

STOCK3S-68420 IBS

BAS 06632970 Asinex

ASN 17325346 Asinex

STOCK4S-01916 IBS

ST4093613 TimTech

Although the invention has been described and illustrated in theforegoing illustrative embodiments, it is understood that the presentdisclosure has been made only by way of example, and that numerouschanges in the details of implementation of the invention can be madewithout departing from the spirit and scope of the invention, which islimited only by the claims that follow. Features of the disclosedembodiments can be combined and rearranged in various ways to obtainadditional embodiments within the scope and spirit of the invention.

What is claimed is:
 1. A method for treating nervous system cancer in asubject in need thereof comprising administering to the subject acompound that inhibits a MGES protein.
 2. The method of claim 1, whereinthe compound is selected from the group consisting of etoposide,5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 3. The method of claim 2,wherein the compound is selected from the group consisting of5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 4. The method of claim 3,wherein the compound is selected from the group consisting ofClostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 5. The method of claim 1,wherein the MGES protein is C/EPB or Stat3.
 6. The method of claim 1,wherein the cancer is glioma or meningioma.
 7. The method of claim 1,wherein the cancer is astrocytoma, Glioblastoma Multiforme,oligodentroglioma, ependymoma or meningioma.
 8. The method of claim 1,wherein the cancer is cerebellar astrocytoma, medulloblastoma,ependymona, brain stem glioma, optic nerve glioma, acoustic neuromas,nerve sheath tumors, or germinoma.
 9. A method for decreasing MGESprotein activity in a subject having a nervous system cancer, the methodcomprising administering to the subject a compound that inhibits a MGESprotein.
 10. The method of claim 9, wherein the compound is selectedfrom the group consisting of etoposide, 5-fluorouracil, Clostridiumdifficile Toxin B,

and pharmaceutically acceptable salts thereof.
 11. The method of claim10, wherein the compound is selected from the group consisting of5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 12. The method of claim11, wherein the compound is selected from the group consisting ofClostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 13. The method of claim9, wherein the MGES protein is C/EPB or Stat3.
 14. The method of claim9, wherein the cancer is glioma or meningioma.
 15. The method of claim9, wherein the cancer is astrocytoma, Glioblastoma Multiforme,oligodentroglioma, ependymoma or meningioma.
 16. The method of claim 9,wherein the cancer is cerebellar astrocytoma, medulloblastoma,ependymona, brain stem glioma, optic nerve glioma, acoustic neuromas,nerve sheath tumors, or germinoma.
 17. A method for inhibiting a MGESprotein comprising contacting said protein with an effective amount of acompound selected from the group consisting of etoposide,5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 18. The method of claim17, wherein the compound is selected from the group consisting of5-fluorouracil, Clostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 19. The method of claim18, wherein the compound is selected from the group consisting ofClostridium difficile Toxin B,

and pharmaceutically acceptable salts thereof.
 20. The method of claim17, wherein the MGES protein is C/EPB or Stat3.
 21. A method fordetecting the presence of or a predisposition to a nervous system cancerin a human subject, the method comprising: (a) obtaining a biologicalsample from a subject; and (b) detecting whether or not there is analteration in the expression of a Mesenchymal-Gene-Expression-Signature(MGES) gene in the subject as compared to a subject not afflicted with anervous system cancer.
 22. The method of claim 21, wherein the MGES genecomprises Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238, or acombination thereof.
 23. The method of claim 21, wherein the detectingcomprises detecting in the sample whether there is an increase in a MGESmRNA, a MGES polypeptide, or a combination thereof.
 24. The method ofclaim 23, wherein the MGES gene comprises Stat3, C/EBPβ, C/EBPδ, RunX1,FosL2, bHLH-B2, or a combination thereof.
 25. The method of claim 21,wherein the detecting comprises detecting in the sample whether there isa decrease in a MGES mRNA, a MGES polypeptide, or a combination thereof.26. The method of claim 25, wherein the MGES gene comprises ZNF238. 27.The method of claim 21, wherein the nervous system cancer comprises aglioma.
 28. The method of claim 27, wherein the glioma comprises anastrocytoma, a Glioblastoma Multiforme, an oligodendroglioma, anependymoma, or a combination thereof.
 29. A method for inhibitingproliferation of a nervous system tumor cell or for promotingdifferentiation of a nervous system tumor cell, the method comprisingdecreasing the expression of a Mesenchymal-Gene-Expression-Signature(MGES) molecule in a nervous system tumor cell, thereby inhibitingproliferation or promoting differentiation.
 30. The method of claim 29,wherein the proliferation comprises cell invasion, cell migration, or acombination thereof.
 31. A method for inhibiting angiogenesis in anervous system tumor, the method comprising decreasing the expression ofa Mesenchymal-Gene-Expression-Signature (MGES) molecule in a nervoussystem tumor cell, thereby inhibiting angiogenesis.
 32. A method fortreating a nervous system tumor in a subject, the method comprisingadministering to a nervous system tumor cell in the subject an effectiveamount of a composition that decreases the expression of aMesenchymal-Gene-Expression-Signature (MGES) molecule in a nervoussystem tumor cell, thereby treating nervous system tumor in the subject.33. A method for identifying a compound that binds to aMesenchymal-Gene-Expression-Signature (MGES) protein, the methodcomprising: a) providing an electronic library of test compounds; b)providing atomic coordinates for at least 20 amino acid residues for thebinding pocket of the MGES protein, wherein the coordinates have a rootmean square deviation therefrom, with respect to at least 50% of Cαatoms, of not greater than about 5 Å, in a computer readable format; c)converting the atomic coordinates into electrical signals readable by acomputer processor to generate a three dimensional model of the MGESprotein; d) performing a data processing method, wherein electronic testcompounds from the library are superimposed upon the three dimensionalmodel of the MGES protein; and e) determining which test compound fitsinto the binding pocket of the three dimensional model of the MGESprotein, thereby identifying which compound binds to theMesenchymal-Gene-Expression-Signature (MGES) protein.
 34. The method ofclaim 33, further comprising: f) obtaining or synthesizing the compounddetermined to bind to the Mesenchymal-Gene-Expression-Signature (MGES)protein or to modulate MGES protein activity; g) contacting the MGESprotein with the compound under a condition suitable for binding; and h)determining whether the compound modulates MGES protein activity using adiagnostic assay.
 35. The method of claim 33, wherein the MGES proteincomprises Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238
 36. Themethod of claim 33, wherein the compound is a MGES antagonist or MGESagonist.
 37. The method of claim 36, wherein the antagonist decreasesMGES protein or RNA expression or MGES activity by at least about 10%,at least about 20%, at least about 30%, at least about 40%, at leastabout 50%, at least about 60%, at least about 70%, at least about 75%,at least about 80%, at least about 90%, at least about 95%, at leastabout 99%, or 100%.
 38. The method of claim 36, wherein the antagonistis directed to Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2 or acombination thereof.
 39. The method of claim 36, wherein the agonistincreases MGES protein or RNA expression or MGES activity by at leastabout 10%, at least about 20%, at least about 30%, at least about 40%,at least about 50%, at least about 60%, at least about 70%, at leastabout 75%, at least about 80%, at least about 90%, at least about 95%,at least about 99%, or 100%.
 40. The method of claim 36, wherein theagonist is directed to ZNF238.
 41. A compound identified by the methodof claim 33, wherein the compound binds to the active site of MGES. 42.A method for decreasing MGES gene expression in a subject having anervous system cancer, the method comprising: a) administering to thesubject an effective amount of a composition comprising a MGES inhibitorcompound, thereby decreasing MGES expression in the subject.
 43. Themethod of claim 33 or claim 42, wherein the compound comprises anantibody that specifically binds to a MGES protein or a fragmentthereof; an antisense RNA or antisense DNA that inhibits expression ofMGES polypeptide; a siRNA that specifically targets a MGES gene; a shRNAthat specifically targets a MGES gene; or a combination thereof.
 44. Adiagnostic kit for determining whether a sample from a subject exhibitsincreased or decreased expression of at least 2 or more MGES genes, thekit comprising nucleic acid primers that specifically hybridize to anMGES gene, wherein the primer will prime a polymerase reaction only whena nucleic acid sequence comprising any one of SEQ ID NOS: 232, 234, 236,238, 240, 242, or 244 is present.
 45. The kit of claim 44, wherein theMGES gene is Stat3, C/EBPβ, C/EBPδ, RunX1, FosL2, bHLH-B2, ZNF238, or acombination thereof.